The Indonesian Ministry of Higher Education, Science, and Technology (Kemdikbudristek) mandates that all higher education institutions periodically report academic data to ensure quality assurance and support evidencebased policymaking. To facilitate this process, the government introduced NeoFeeder, a centralized data reporting system for submitting data to the national Higher Education Database (PDDIKTI). Reporting is conducted biannually and serves as a key requirement for institutional operational compliance. Despite its importance, many higher education institutions face significant challenges in fulfilling this obligation. Administrative staf are responsible for various tasks, including data collection, validation, and manual entry. These responsibilities often lead to excessive workloads, increased risk of human error, and compromised data integrity. Recognizing these challenges, SEVIMA, a Surabaya-based startup, developed GoFeeder and ProFeeder-applications designed to streamline the data reporting process by integrating academic information systems and automatic synchronization with NeoFeeder. This process redesign has proven to be highly beneficial for university administrators. This study employs a qualitative case study approach, utilizing semi-structured interviews and document analysis; including technical documentation and reporting performance data; to explore the implementation of the redesigned process and its outcomes. Based on the findings, three key impacts of the process redesign were identified: (1) improved data quality, reflected in more accurate and complete submissions to PDDIKTI; (2) cost eficiency, with operational savings of up to 52% calculated for participating institutions; and (3) a 97% reduction in carbon emissions, projected through modeling, aligning with national initiatives to adopt environmentally sustainable technologies.
Based on the Ministry of Higher Education, Science, and Technology (Kemdiktisaintek) database, in 2024
Indonesia will have 4,363 higher education institutions [1], this number is the second-largest number
of universities globally after India. All these institutions are supervised by the Directorate General
of Higher Education, Research, and Technology (Kemdiktisaintek) through biannual data reporting
submitted via the Higher Education Database System (Pangkalan Data Pendidikan Tinggi, or PDDIKTI)
[
From the perspective of Business Process Management (BPM) discipline, ineficiency in the manual
reporting process carried out by university operators is something that can be improved through
process redesign [
The remainder of this paper is organized as follows: Section 2 explores the key challenges faced by Indonesian higher education institutions to fulfill their reporting obligations; Section 3 discusses the redesign and digitization of the data reporting process through SEVIMA’s solutions; Section 4 outlines lessons learned, followed by an analysis of their significance, relevance, and limitations; and Section 5 concludes with final reflections and future research directions.
Mandated by Regulation No. 61/2016 of the Minister of Research, Technology, and Higher Education of the Republic of Indonesia, the use of NeoFeeder for institutional data reporting has become a nationwide requirement. Non-compliance may lead to administrative sanctions, including delays in accreditation or invalidation of diploma serial numbers. Various challenges encountered by higher education institutions in fulfilling this mandate are examined in the following subsections.
Many small universities still do not possess a dedicated academic information system. Their reporting
process relies entirely on one or two staf members who manually enter data into NeoFeeder using
basic computing equipment, typically laptops. These devices are prone to overheating, data loss, or
inconsistent performance. Moreover, the limited number of operators and tools contributes to fatigue,
increasing the likelihood of human error. Medium and large universities are somewhat more advanced,
often having internal academic systems that allow them to extract and upload digital data. However,
even with this advantage, operators must still convert and upload datasets in formats compatible with
NeoFeeder, one by one. This process requires a high degree of precision and concentration. Errors in
formatting or sequencing often result in failed uploads or incorrect reporting, further complicating
compliance eforts. Connectivity issues further exacerbate these problems, particularly in remote or
underserved regions of Indonesia. Institutions in such areas may face frequent interruptions or slow
internet speeds, making it dificult to complete reporting tasks, especially during periods close to the
oficial submission deadline, when server trafic surges. Several studies have identified these dificulties
and proposed solutions, including those by [
The implementation of NeoFeeder, a relatively new application launched in 2022, has presented
significant challenges for many university operators. Most are still in the process of familiarizing themselves
with the system through trial and error. Entering academic report data every semester remains one
of the most demanding tasks, especially when followed by manual validation within the NeoFeeder
system. These operational burdens are further compounded by the limited digital skills among staf in
many higher education institutions. This issue is particularly evident in institutions that lack suficient
competence in information technology. For example, some universities still rely on teaching and
administrative staf whose abilities to develop and manage digital systems remain very limited [
The previous challenges might be more manageable if higher education institutions had suficient ifnancial resources. With adequate funding, universities could invest in robust academic information systems, develop or procure middleware to integrate these systems with NeoFeeder, and build capable IT teams to oversee digital reporting operations. Unfortunately, financial constraints remain a persistent barrier for many institutions across Indonesia. Out of approximately 4,363 higher education institutions in the country, only 128 (2.9%) are public universities that receive direct government support [1]. The remaining 97% are private institutions that must independently secure funding to sustain operations. These private universities are often required to be highly resourceful in managing limited budgets while still striving to deliver quality education. In such circumstances, investing in IT infrastructure and skilled digital talent becomes a considerable challenge. Many institutions are forced to prioritize operational and instructional costs over technological upgrades, which in turn hampers their ability to modernize reporting systems and meet regulatory compliance eficiently.
This section employs a qualitative case study approach, as outlined by Yin [
The steps for reporting data to PDDIKTI manually are illustrated in Figure 1, representing what we call the traditional ecosystem, which relies heavily on the manual eforts of operators. This manual reporting process can be broken down into six key steps, as shown in the figure above. The workflow is linear, isolated, and repetitive, placing the entire burden of ensuring data accuracy and timeliness on the operators.
1. Collect academic data manually: Operators gather academic information from various institutional units, often relying on paper-based or disconnected sources. 2. Digitize the collected data: The raw data is then converted into digital form, typically through manual entry into spreadsheets or databases. 3. Enter data into the NeoFeeder system: Data is manually uploaded to NeoFeeder, frequently one record at a time, following strict formatting requirements. 4. Check data validity: Operators perform data validation to identify missing or incorrect information. 5. Perform data correction: Detected errors are corrected manually, often requiring verification against original source documents. 6. Initiate synchronization to the PDDIKTI server: After ensuring data accuracy, operators synchronize the data with the central PDDIKTI server.
This sequence of manual, repetitive tasks creates significant opportunities for errors and delays, ultimately compromising the quality and timeliness of higher education data reporting. According to Regulation No. 61/2016 issued by the Ministry of Research, Technology, and Higher Education, the reporting process is intended to be bidirectional, involving continuous data synchronization between the central government system and institutional academic systems. For student activity data, such as semester enrollments and academic grades, campus operators are responsible for submitting data to the central system, for master data, such as new study programs, improved lecturer profiles, and newly established campuses, are transmitted from the central system to institutional systems via NeoFeeder. Manual synchronization of this data demands significant efort and poses a high risk of human error.
The manual process has seen some improvement with the emergence of a digital ecosystem, where operators are no longer required to digitize paper-based academic data, since academic activities are now recorded through information systems. With these systems in place, lecturers and students independently report their activities, relieving operators from manually entering data as was necessary in the traditional ecosystem. However, due to structural incompatibilities between the internal academic systems and NeoFeeder’s data requirements, operators still need to manually download the data and re-upload it record by record, as illustrated in Figure 2. This situation continues to impose significant stress on operators, as manually checking data demands considerable concentration and efort.
In response to the challenges identified in both the traditional manual process and the initial digital ecosystem, Figure 3 illustrates a redesigned academic data reporting process characterized by full integration and automation. Within this fully digital ecosystem, interoperability and actor collaboration play a central role. Academic data is no longer manually compiled after the learning process; rather, it is generated organically during the learning activities themselves. As lecturers conduct academic activities and students complete assignments, these actions are directly recorded within the academic information system. Operators are now responsible only for initiating schedules and validating data prior to synchronization, as learning-related data flows automatically into NeoFeeder without redundant re-entry. As shown in Figure 3, two previously critical activities, downloading academic data and reentering it into NeoFeeder, as seen in Figures 1 and 2, are entirely eliminated. This redesign not only reduces human error but also shortens reporting cycles and enhances data traceability. The result is a more eficient, integrated, and actor-driven reporting mechanism in which responsibilities are clearly distributed and supported by system automation. ProFeeder and GoFeeder also assist operators in validating data more eficiently by providing dashboards that display data comparison results, eliminating the need for manual one-by-one checks. Further verification is only required when the dashboard notifies users of potential data inconsistencies.
SEVIMA’s innovation in eliminating two previously manual and labor-intensive activities, namely,
downloading academic data from internal systems and re-entering it into NeoFeeder, falls within the
analytical-transformational quadrant, a domain commonly associated with Business Process
Reengineering (BPR), as illustrated in the “Redesign Orbit” [
1. Visioning: SEVIMA began by recognizing the national challenge posed by burdensome and errorprone academic reporting processes mandated by the Ministry of Research and Higher Education. A vision emerged to automate and streamline this process at scale through a technology-driven approach. 2. Identifying: The team systematically identified pain points in the existing practices, particularly the manual extraction of data from local systems and the repetitive re-uploading to NeoFeeder. 3. Analyzing: A technical analysis was conducted on the NeoFeeder API and the associated validation logic to derive a system architecture capable of automating data consistency checks and ensuring seamless synchronization. 4. Redesigning: SEVIMA developed GoFeeder and ProFeeder, two cloud-based services designed to act as middleware, bridging internal academic systems with NeoFeeder while automating critical steps in the reporting pipeline. 5. Evaluating: Early-stage product launches were conducted to assess market response. The solution gained rapid traction, supported by high adoption rates due to its alignment with institutional needs and regulatory requirements. 6. Implementing: The solution was rolled out across multiple campuses, replacing fragmented manual workflows with an integrated, automated reporting pipeline. 7. Improving: Continuous feedback loops and updates to national regulations are actively monitored.
SEVIMA regularly enhances both tools with new features such as validation engines, reporting dashboards, and user assistance modules.
This chapter presents the key findings derived from the data collection and analysis, highlighting the practical implications of SEVIMA’s process redesign in the context of Indonesian higher education. It outlines the observed impacts on academic data reporting, summarizes the lessons learned from implementation practices, and identifies the scope and limitations of the study. Together, these findings provide insights that may inform future improvements in digital reporting systems and guide similar initiatives in other educational contexts.
The implementation of SEVIMA’s redesigned reporting system has led to a range of improvements in how academic data is processed and managed across higher education institutions. This section outlines the specific areas where these changes have had the most significant impact, particularly in enhancing data accuracy, reducing operational costs, and promoting more sustainable reporting practices.
One of the strategic focuses in redesigning the academic reporting process is to improve data quality in order to support better decision-making at the national level. As illustrated in Figure 3, the introduction of a digital ecosystem enables the reporting process to become more integrated, traceable, and accountable. In both traditional and early digital systems, academic data was managed manually, which allowed for inconsistencies and, in some cases, data manipulation. For example, some institutions might intentionally alter enrollment or graduation numbers to meet accreditation or funding criteria. This undermines the integrity of national education data and weakens policy formulation eforts. To address this challenge, SEVIMA’s redesigned ecosystem adopts a local validation approach, ensuring that data is verified and cleaned before being transmitted to the government’s centralized database (PDDIKTI). With GoFeeder, data flows directly from campus academic systems to NeoFeeder without repeated manual entry, significantly reducing opportunities for manipulation. Every change and submission is automatically recorded and time-stamped, enabling traceability and auditability. This shift not only reduces the operational burden on campus operators but also ensures that the government receives high-quality, trustworthy data in near real-time. The system promotes transparency while supporting regulatory bodies in performing timely evaluations, forecasting education trends, and allocating resources more efectively.
On the campus side, their choice to use SEVIMA’s products to facilitate the reporting process as well as
implement innovation and digitalization has led to significant budget eficiency. Figure 5 illustrates
the substantial costs that campuses must bear when procuring their own servers. They typically need
to invest around 17 million IDR per month (based on the illustration in [
A key factor driving eficiency in SEVIMA’s cloud platform is its multi-tenant architecture, which enables multiple universities to share the same software instance securely and independently. Unlike monolithic systems where resources are siloed, this design allows for resource optimization without compromising data privacy or tenant-specific customization. Central to this ecosystem is a centralized data hub that collects, processes, and distributes data in real time across participating campuses. This hub ensures consistent and accurate data exchange, streamlining integration and reporting processes through NeoFeeder. SEVIMA partners with cloud service providers who prioritize renewable energy and sustainable operational practices, thereby contributing to a reduced carbon footprint. Continuous monitoring and optimization enable the platform to support institutions in achieving their sustainability goals without sacrificing performance or reliability. By leveraging shared cloud resources among thousands of campuses via the multi-tenant system, SEVIMA promotes energy-eficient data center operations and efective resource allocation. This fractional-cost cloud service model aligns energy consumption closely with actual usage, minimizing waste compared to traditional on-premises infrastructures where individual campuses maintain separate, often underutilized servers. Moreover, the centralized data hub reduces redundant data processing and enhances overall system eficiency, putting green energy principles into practice. This sustainable approach not only supports global environmental commitments but also yields cost savings by lowering energy bills and operational expenses, ultimately fostering a more responsible and sustainable IT ecosystem for the higher education sector. An illustration of the carbon footprint calculation is provided in Figure 6, which shows the emission levels if each campus were to manage its own server. When comparing both figures, SEVIMA’s centralized server system achieves a significant reduction in carbon emissions up to 97%.
The innovations introduced by SEVIMA through the GoFeeder and ProFeeder platforms hold significant value in the context of digital transformation in Indonesian higher education. Without these solutions, many institutions would likely experience resource ineficiencies due to cumbersome reporting processes and limited system interoperability. SEVIMA’s fractional-cost cloud model serves as a strategic solution, enabling institutions to pay only for the resources they consume. This approach efectively reduces capital expenditure and fosters a more inclusive adoption of technology, particularly for institutions with limited financial capacity.
The process redesign supported by GoFeeder and ProFeeder has led to tangible improvements in data
accuracy, stakeholder collaboration, and operational eficiency. This aligns with the process redesign
principles discussed by [
While the implemented solutions have demonstrated positive outcomes, several limitations and scope boundaries were identified. The interoperability framework relies heavily on the compatibility and standardization of data formats across systems, which can pose challenges in heterogeneous IT environments. Additionally, the benefits of the fractional-cost cloud model are optimized when usage patterns are well understood; unpredictable or highly variable workloads may afect cost eficiency. This paper primarily focuses on mid-sized organizations utilizing SEVIMA’s cloud platform, which may limit the generalizability of the findings to larger enterprises or users of other cloud providers. Future research should explore the adaptability of these solutions in diverse organizational contexts and assess their long-term impact on operational resiliency and innovation capacity.
This paper demonstrates that interoperable systems and fractional-cost cloud solutions are key enablers for improving academic data reporting processes in higher education and fostering broader digital innovation. In the context of Indonesian higher education, integrating diverse academic information systems significantly enhances data accuracy, accelerates reporting workflows, and fosters more seamless collaboration among stakeholders. SEVIMA’s cloud-based services contribute to cost eficiency, allowing institutions to scale their operations while reallocating resources toward innovation and quality improvement. Notably, GoFeeder has achieved a success rate of approximately 95.05%, while ProFeeder reaches an even higher success rate of 98.57%, underscoring the reliability of these platforms in supporting academic data processes. However, the success of such transformation depends on data standardization, system compatibility, and a deep understanding of user behavior and institutional processes. Overall, these technologies form a robust foundation for digital transformation in higher education, positioning institutions to achieve greater operational eficiency and long-term competitive advantage.
The author(s) used Generative AI tools to assist in checking grammar and improving the clarity of the language. [1] PDDikti, Pangkalan data pendidikan tinggi [higher education database system], direktorat jenderal pendidikan tinggi riset dan teknologi [directorate general of higher education research and technology], 2025. URL: https://pddikti.kemdiktisaintek.go.id/.