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All Journal ELINVO (Electronics, Informatics, and Vocational Education) Journal Automation Computer Information System (JACIS) Advance Sustainable Science, Engineering and Technology (ASSET)
Adhika Pramita Widyassari
Sekolah Tinggi Teknologi Ronggolawe
Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Integrating Psychological Stress Indicators with Academic Data for Student Dropout Prediction: A Decision Tree and Expert System Approach Indra Gunawan; Adhika Pramita Widyassari
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 10 No. 2 (2025): November 2025
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v10i2.89031

Abstract

Student attrition remains a major problem in higher education. Although academic variables are well-established moderators, psychological wellness, especially stress, is an important but often ignored moderator. The purpose of this study is to construct prediction models for students at risk of dropping out by combining academic and psychological information. One major challenge in this field is the class imbalance of student records, which results in a significant drop in the dropout rate compared to the general population. Therefore, in this study, we employ a Decision Tree algorithm and use a Forward Chaining inference engine along with the Synthetic Minority Over-sampling Technique (SMOTE) to solve it. We employed a data set of 122 students at one institution, with psychological stress scores generated from a standardised questionnaire according to well-known symptom domains. The accuracy for the model with only a Decision Tree was 95.83%. For the stress score, integration with the FC-based attribute increased performance to 96.67%; however, this model exhibited only marginal improvement over the final model due to its very low accuracy when compared to that of SMOTE. This ensemble model performed the best with an accuracy of 97.50% and an AUC of 96.35%. This progression demonstrates that even though the introduction of psychological information is beneficial, an approach to balance data and ensure a robust prediction system is required. This article is a proof-of-concept analysis which creates an opportunity for universities to establish proactive, early-warning-driven models; yet there is a requirement for future validation studies on larger and more diversified samples.
Hybrid Expert System for Academic Stress Diagnosis Using Forward Chaining and Score Weighting Indra Gunawan; Adhika Pramita Widyassari; Ismail Yusuf Panessai; Jonathan Rante Carreon
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2586

Abstract

Academic stress classification is a significant challenge in education, as previous approaches often rely on opaque models or require large training datasets. This study develops a hybrid expert system for academic stress classification using forward chaining and Certainty Factor (CF) score fallback. The system was tested on 100 student cases with the following label distributions: Mild (48), Moderate (37), and High (13), classified independently by three experts. Label validity was tested using pairwise Cohen's kappa, yielding a mean value of 0.8280. The system achieved 100% accuracy, a 32% improvement over the classical forward chaining baseline (68%). Statistical evaluation using Wilson score intervals demonstrated high consistency across all key metrics (accuracy, precision, recall, F1-score) with a 95% CI of [96.4%, 100%]. The system is designed with an explicit and auditable rule structure, enabling deterministic classification based on symptoms. Although validation results are high, the unbalanced label distribution opens up the potential for spectrum bias. Going forward, the system is planned to be tested across institutions, assessed for integration with counseling services, and compared with other hybrid approaches. 
Implementasi Metode Certainty Factor dan TOPSIS pada Diagnosis Penyakit Jagung dan Penentuan Prioritas Penanganan Uma Fadhila Dina Puspita; Adhika Pramita Widyassari
Journal Automation Computer Information System Vol. 6 No. 1 (2026): Mei
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v6i1.181

Abstract

Jagung merupakan salah satu komoditas penting dalam sektor pangan dan agribisnis, tetapi produktivitasnya masih sering terganggu oleh serangan penyakit yang memiliki gejala serupa. Kondisi ini menyebabkan proses identifikasi penyakit dan penentuan penanganan yang tepat menjadi tidak mudah. Penelitian ini bertujuan mengembangkan sistem berbasis web yang dapat membantu diagnosis penyakit tanaman jagung sekaligus menentukan prioritas penanganan yang sesuai. Sistem dirancang dengan menggabungkan metode Certainty Factor untuk menghitung tingkat keyakinan diagnosis berdasarkan gejala yang dipilih pengguna, serta metode Technique for Order Preference by Similarity to Ideal Solution untuk menyusun urutan prioritas solusi berdasarkan beberapa kriteria keputusan. Basis pengetahuan sistem mencakup enam jenis penyakit, lima belas gejala, dan delapan belas alternatif penanganan. Pengujian dilakukan menggunakan 30 kasus uji berbasis pengetahuan pakar, lalu dievaluasi dengan accuracy, precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa sistem mampu menghasilkan diagnosis yang sesuai dengan label pakar pada seluruh kasus uji, dengan nilai accuracy, precision, recall, dan F1-score sebesar 1,00. Selain itu, metode TOPSIS juga mampu menghasilkan rekomendasi penanganan yang lebih terstruktur sesuai preferensi pengguna. Dengan demikian, sistem yang dikembangkan dapat mendukung proses diagnosis penyakit jagung dan membantu pengguna menentukan prioritas penanganan secara lebih sistematis dan terukur.
Implementasi Metode Simple Additive Weighting (SAW) Untuk Pemilihan Smartphone Gaming Ahmad Himam Auliya; Adhika Pramita Widyassari
Journal Automation Computer Information System Vol. 6 No. 1 (2026): Mei
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v6i1.182

Abstract

Maraknya pilihan smartphone gaming di pasaran dengan ragam spesifikasi dan kisaran harga yang beragam kerap menimbulkan kebingungan bagi konsumen dalam menentukan perangkat yang paling tepat sesuai kebutuhan mereka. Penelitian ini bertujuan mengimplementasikan metode Simple Additive Weighting (SAW) dalam sistem pendukung keputusan berbasis web yang dikhususkan untuk pemilihan smartphone gaming. Metode SAW dipilih karena kesederhanaannya dalam melakukan perangkingan alternatif berdasarkan beberapa kriteria, yaitu harga (cost), RAM, skor AnTuTu, kapasitas baterai, dan refresh rate layar (benefit). Data spesifikasi diperoleh melalui studi dokumentasi dari situs resmi produsen dan portal teknologi GSMArena sebanyak 15 smartphone dari merek Asus, Xiaomi, Samsung, iQOO, dan Nubia dengan kriteria inklusi rilis tahun 2023-2025, prosesor Snapdragon 8 series atau MediaTek Dimensity 9000, dan RAM minimal 8 GB. Bobot kriteria diperoleh dari survei kepada 10 responden gamer. Hasil penelitian menunjukkan sistem memiliki akurasi 100% dalam mencocokkan hasil perhitungan dengan perhitungan manual. Nubia Red Magic 9 Pro terpilih sebagai smartphone gaming terbaik dengan nilai preferensi tertinggi 0,8656. Sistem ini diharapkan dapat membantu konsumen dalam memilih perangkat yang sesuai dengan kebutuhan dan anggaran.
Co-Authors Ahmad Himam Auliya Indra Gunawan Ismail Yusuf Panessai Jonathan Rante Carreon Uma Fadhila Dina Puspita