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All Journal Syntax Literate: Jurnal Ilmiah Indonesia Health Information : Jurnal Penelitian PREPOTIF : Jurnal Kesehatan Masyarakat Academic Journal of Computer Science Research (AJCSR) Jurnal Sehat Indonesia (JUSINDO) JURNAL EKONOMI BISNIS DAN MANAJEMEN (EKO-BISMA) Journal of Intelligent Software Systems SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal Intelek Dan Cendikiawan Nusantara Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) JPMB : Jurnal Pengabdian Masyarakat Berkelanjutan
Dodi Syaripudin
Politeknik Pajajaran Insan Cinta Bangsa Bandung
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Studi Pedagogis dan Etika Teknologi pada Penggunaan Sistem Penilaian Esai Berbasis AI di Pendidikan Tinggi Rizki Adha; Lusianto Lusianto; Dodi Syaripudin; Agus Nursikuwagus; Usep Mohamad Ishaq; Andrias Darmayadi
Academic Journal of Computer Science Research Vol 8, No 2 (2026): Academic Journal of Computer Science Research (AJCSR)
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/ajcsr.v8i2.16265

Abstract

Penggunaan kecerdasan artifisial (AI) dalam penilaian esai semakin berkembang di pendidikan tinggi karena kemampuannya meningkatkan efisiensi dan konsistensi evaluasi. Namun, penerapannya menimbulkan tantangan pedagogis dan etika terkait keadilan, transparansi, serta legitimasi keputusan akademik. Penelitian ini bertujuan menganalisis implikasi pedagogis dan etika penggunaan sistem penilaian esai berbasis AI dengan mengintegrasikan persepsi mahasiswa dan dosen. Penelitian menggunakan pendekatan mixed-methods exploratory melalui survei kuantitatif terhadap 36 mahasiswa dan 24 dosen, dilengkapi analisis kualitatif. Hasil menunjukkan mahasiswa memiliki persepsi positif terhadap kegunaan, kemudahan, dan nilai pedagogis, namun tetap berhati-hati terhadap aspek kepercayaan dan transparansi. Sementara itu, dosen menunjukkan tingkat penerimaan sangat tinggi selama sistem diterapkan dalam kerangka terkontrol. Integrasi temuan menunjukkan bahwa pendekatan Human-in-the-Loop merupakan faktor kunci menjaga keseimbangan antara efisiensi teknologi, nilai pedagogis, dan legitimasi akademik. Penelitian ini berkontribusi dengan menegaskan bahwa sistem penilaian esai berbasis AI paling tepat diterapkan dalam paradigma AIassisted assessment, di mana dosen tetap memegang kendali utama dalam pengambilan keputusan akademik, serta memberikan rekomendasi praktis bagi institusi dalam merancang kebijakan penilaian berbasis AI yang bertanggung jawab.
ANALISIS PROSEDUR PENGELOLAAN PENGADAAN BARANG (METODE PEMBAYARAN UP) DAN KEWAJIBAN PERPAJAKANNYA DI PENGADILAN TINGGI BANDUNG Luqman Ali Al-ghifari; Gunardi Gunardi; Priatna Kesumah; Dodi Syaripudin
JURNAL EKONOMI BISNIS DAN MANAJEMEN (EKO-BISMA) Vol 4 No 2 (2025): JURNAL EKONOMI BISNIS DAN MANAJEMEN (EKO-BISMA)
Publisher : PUBLISHER ABISATYA DINAMIKA ISWARA PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58268/eb.v4i2.204

Abstract

This study examines the Analysis of goods procurement procedures and the collection mechanism of Tax Obligations at Pengadilan Tinggi Bandung. Using a descriptive qualitative approach through interviews, observations, and document analysis, the research reveals that the institution has implemented a procurement system divided into two payment methods: Uang Persediaan (UP) for transactions below Rp10 million and SPM-LS for higher-value transactions. The procurement process demonstrates good governance in terms of planning, execution, and financial accountability. However, discrepancies were found in the reporting of PPh 22 periodic tax returns through the DJP Online system, attributed to human resource limitations, heavy workload of treasury staff, and technical issues with computer equipment. Regarding supervision, the role of the Tax Office was deemed suboptimal in monitoring compliance. The study recommends improvements to the electronic reporting system, human resource capacity building through training, and strengthening both internal and external monitoring mechanisms to ensure tax compliance.
Explainable Machine Learning Pipeline for Early Warning of Student Dropout in Graduate Programs Using SHAP and Gradient Boosting Dodi Syaripudin; Dede Hendrik; Andriyana Andriyana; Karya Suhada
Journal of Intelligent Software Systems Vol 5, No 1 (2026): July 2026
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v5i1.2805

Abstract

Student attrition in graduate programs is costly for students and institutions alike, yet most predictive models deployed for early warning behave as black boxes that program managers cannot audit. This paper proposes an end-to-end explainable machine learning pipeline for the early detection of dropout risk in master’s degree programs. The pipeline integrates academic, engagement, and administrative features available at the end of the first semester; trains a gradient boosting classifier with cross validated hyperparameter selection against logistic regression and random forest baselines; calibrates a cost sensitive alert threshold; and attaches Shapley additive explanations (SHAP) to every alert at both global and individual levels. The pipeline is evaluated in a controlled simulation study of 1,200 synthetic student records whose generative process mimics graduate-program registrar and learning management system data, including nonlinear threshold and interaction effects. On a held-out test set, the tuned gradient boosting model attains an area under the ROC curve of 0.790 with a recall of 0.613 at the calibrated threshold, comparable to the strongest baseline, while providing exact, efficient TreeSHAP explanations. Global SHAP analysis correctly recovers first-semester GPA, attendance, and engagement as the dominant risk drivers embedded in the simulation, and local explanations translate individual alerts into actionable counseling points. The results indicate that explanation quality, rather than raw discrimination alone, is the decisive ingredient for adoption of dropout early warning systems by study program managers. Keywords: Dropout pred
Co-Authors Abu Walad Adam Mukharil Bachtiar Agung Rachmat Raharja Agus Nursikuwagus Aji Permana, Aji Andrias Darmayadi Andriyana Andriyana Bobi Kurniawan S Dede Hendrik Ednawati Rainarli Encep Yayat Estiko Rijanto Faldy Irwiensyah Gunardi Ira Febriyanti Irawan Afrianto Irfan Dwiguna Sumitra Jayadi Jayadi Karya Suhada Luqman Ali Al-ghifari Lusianto Lusianto Maulana, Hanhan Permana, Budi priatna kesumah Rizki Adha Rusdi Doviyanto Sindrawati Usep Mohamad Ishaq Yulianti, Delia Zuhri Halim