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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Systems and Informatics Jurnal Ilmiah Ilmu Terapan Universitas Jambi
Marzuqi, Tubagus Ahmad
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Computer Science & IT Education Engineering Industrial & Manufacturing Engineering

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Prediksi Mahasiswa Drop-Out Di Universitas XYZ Marzuqi, Tubagus Ahmad; Kristiani, Evelline; Marcel
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 6: Desember 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024118689

Abstract

Akreditasi dan reputasi merupakan faktor krusial bagi setiap perguruan tinggi, termasuk Universitas XYZ. Salah satu hal yang dapat memengaruhi akreditasi adalah jumlah mahasiswa yang mengalami drop-out (DO). Untuk mencegah penurunan akreditasi dan reputasi akibat masalah tersebut, penelitian ini berfokus pada pengembangan model prediksi mahasiswa DO. Algoritma Random Forest, Gradient Boosting, dan Decision Tree digunakan untuk mengevaluasi seberapa akurat model klasifikasi dalam memprediksi potensi mahasiswa DO berdasarkan data akademik. Sebelum membangun model, digunakan metode SMOTE untuk menangani masalah ketidakseimbangan data. Hasil penelitian menunjukkan bahwa model prediksi menggunakan algoritma Random Forest berhasil mencapai akurasi sebesar 99,67%. Algoritma Gradient Boosting menghasilkan akurasi 99,21%, sementara Decision Tree mencapai akurasi sebesar 98,67%. Selain mengukur akurasi model, penelitian ini juga mengidentifikasi faktor-faktor utama yang berkontribusi terhadap prediksi mahasiswa DO. Faktor-faktor tersebut meliputi adanya tunggakan pembayaran, IPK rata-rata di bawah 2, jumlah mata kuliah yang diulang lebih dari satu kali, dan kegagalan dalam melakukan KRS lebih dari dua kali. Penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam bidang prediksi akademik, khususnya dalam upaya mengurangi tingkat mahasiswa drop-out (DO) di perguruan tinggi.   Abstract Accreditation and reputation are critical factors for higher education institutions, including XYZ University. One factor that can negatively impact accreditation is the number of students who drop out (DO). To prevent a decline in accreditation and reputation due to this issue, this study aims to develop a predictive model for student dropouts. The Random Forest, Gradient Boosting, and Decision Tree algorithms were utilized to evaluate the accuracy of classification models in predicting potential dropouts using academic baseline data. Prior to model building, the SMOTE method was applied to address the issue of imbalanced data. The results indicate that the predictive model using the Random Forest algorithm achieved an accuracy of 99.67%. The Gradient Boosting algorithm yielded an accuracy of 99.21%, while the Decision Tree algorithm achieved 98.67% accuracy. In addition to determining model accuracy, this study also identified key factors contributing to the prediction of student dropouts. These factors include outstanding payment history, a GPA below 2.0, repeating courses more than once, and failing to register for courses (KRS) more than twice. This research is expected to make a significant contribution to the field of academic prediction, particularly in efforts to reduce the dropout (DO) rate among university students.  
A Novel UX-Centered ITSM Framework for Technology Startups: Beyond Traditional Service Management Marcel, Marcel; Marzuqi, Tubagus Ahmad
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1118

Abstract

This research explores the integration of User Experience (UX) principles into IT Service Management (ITSM) frameworks within resource-constrained B2B SaaS technology startups. Through a comprehensive qualitative case study methodology involving semi-structured interviews with seven stakeholders, participatory observation across 12 sessions, and systematic document analysis at a Jakarta-based startup serving SMEs, we uncovered a critical paradox: companies selling superior UX solutions to clients often neglect these principles in internal IT management. The primary contribution is a novel adaptive UX-Centered ITSM conceptual model featuring three interconnected layers: Core Principles, Implementation Domains, and Operational Elements, designed for incremental implementation based on startup capacity. Unlike rigid existing ITSM frameworks, this model introduces a prioritized approach with "Must Have," "Should Have," and "Can Be Added" categorizations specifically tailored for startup contexts. The research identified five contextual factors influencing implementation: organizational culture, leadership structure, resource limitations, team dynamics, and SME client characteristics. Findings reveal that UX-centered ITSM not only addresses internal operational challenges but creates strategic alignment between internal practices and external value propositions, forming the foundation for market credibility and business sustainability. This framework provides startup managers and IT practitioners with an actionable roadmap for transforming ad-hoc internal systems into user-centered services that support operational excellence while enhancing competitive positioning in digital transformation markets.
INFORMAL COORDINATION AS SHADOW WORKFLOW: REWORK AND PROCESS FRICTION IN A SMALL FIRM IT ACCESS Marcel, Marcel; Marzuqi, Tubagus Ahmad
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 10 No. 2 (2026): Volume 10, Nomor 2, April 2026
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v10i2.53984

Abstract

This study examines how operational rework appears in internal access management in a small technology consulting firm in Jakarta, where work processes are still loosely organized. Using a mixed methods approach, the study reviewed 97 operational cases across six months and combined them with interviews with people directly involved in handling access issues. The analysis focused on access requests, password resets or account recovery, and escalations or handoffs, not general service complaints. The results show that the biggest burden comes less from the technical fix itself and more from three transition points: moving cases from WhatsApp to formal channels (41%), waiting for approval (48%), and handing cases to another person in charge (37%). Access requests faced the strongest approval barriers, while escalations and handoffs created the most repeated clarification, case transfers, and cases returning after they seemed finished. The interviews also show that WhatsApp often works as a shadow workflow: fast at the start, messy later when the initial context is not fully recorded, approvals depend on certain intermediaries, or a case looks closed even though the solution is only temporary. The novelty of this study lies in its practical view of rework through three layers, namely entry friction, processing friction, and closure distortion, along with a lightweight diagnostic that small companies can use to spot process waste without needing a complex evaluation system.
Detecting Experience Debt in Internal IT Services: A Minimal Touchpoint-Based XLA for Small EdTech Startups Marcel; Marzuqi, Tubagus Ahmad
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1567

Abstract

Small EdTech startups often use SLA numbers to check whether internal IT services are working well. The problem is that these numbers usually show only whether the service was restored and how long it took. They do not show what users actually experienced while the problem was happening. This study looks at that hidden part. It uses the idea of experience debt to describe small but repeated service problems that slowly make daily work harder. The study was conducted in three small EdTech startups in Jakarta. It used survey data and interview data collected in the same period. The survey produced 274 valid responses across six touchpoints and five experience dimensions. The interviews involved 18 informants and focused on the service moments they remembered as most difficult. The weakest point appeared at TP2, where users faced access problems and often felt confused about what was happening, what to do next, and whether the issue was really finished. At TP4 and TP5, the main problem was fairness. Users often felt stuck in a process they could not see, had to repeat the same explanation, or did not know who was handling their request. From these findings, the study developed a simple touchpoint-based XLA as a one-page review tool. Its purpose is to help small teams notice user experience problems that normal SLA monitoring often misses. More research is still needed in other service settings and organisations.
Co-Authors Kristiani, Evelline Marcel Marcel Marcel