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All Journal Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems) MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Journal Industrial Servicess Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Format : Jurnal Imiah Teknik Informatika Tech-E Jurnal TAM (Technology Acceptance Model) Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat bit-Tech Algor Jurnal Sistem Informasi dan Bisnis Cerdas Inovasi Gagasan Abdimas & Kuliah Kerja Nyata
Benny Daniawan
Universitas Buddhi Dharma
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Transportation Other

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Optimization of Multimodal Deep Learning for Depression Detection Aditiya Hermawan; Benny Daniawan; Edy Edy; Joese Nathaniel
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.111407

Abstract

Depression is a complex and often underdiagnosed mental health condition that manifests through subtle verbal, acoustic, and behavioral cues. Traditional unimodal detection systems struggle to capture the full spectrum of depressive symptoms, often leading to inaccurate or incomplete assessments. This study proposes a multimodal deep learning framework that integrates textual, audio, and visual modalities to improve the robustness and reliability of automatic depression detection, achieving an overall classification accuracy of 74%. The approach prioritizes privacy and interpretability by using facial keypoints and gaze direction rather than raw video frames, and applies attention mechanisms to align and fuse features across modalities. Each modality is processed through dedicated neural architectures tailored to its data type, and their outputs are combined within a fusion model that learns to capture cross-modal emotional patterns. Experimental results demonstrate that the proposed multimodal system significantly outperforms its unimodal counterparts in terms of classification performance. The visual modality was found to contribute most strongly to detection accuracy, as confirmed by ablation analysis. These findings highlight the value of multimodal integration in capturing complex psychological signals and support the development of intelligent, non-invasive screening tools for use in digital mental health applications.
Analisis Komparatif Algoritma Pembelajaran Mesin untuk Mengidentifikasi Keterlambatan Kelulusan Mahasiswa Benny Daniawan; Suwitno Suwitno; Andri Wijaya; Ardiane Rossi Kurniawan Maranto; Junaedi Junaedi
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30186

Abstract

Delayed student graduation is a critical issue in higher education because it affects academic planning, student support, and institutional performance evaluation. This study develops a leakage controlled machine learning framework for early identification of students at risk of delayed graduation, using academic records available through the sixth semester. A dataset of 564 students was used, with graduation status defined as on-time for students graduating in the eighth semester or earlier and delayed for those graduating after the eighth semester. To prevent temporal data leakage, post-outcome variables were excluded from the predictor set. Five supervised learning algorithms were evaluated: Decision Tree, Support Vector Machine, Random Forest, Naïve Bayes, and K-Nearest Neighbor. Preprocessing was performed using one-hot encoding and standardization within a pipeline, and model performance was assessed using stratified five-fold cross-validation. The tuned Random Forest achieved the most balanced performance, with 0.956 accuracy, 0.861 delayed-class precision, 0.805 delayed-class recall, 0.832 delayed-class F1-score, and 0.977 ROC-AUC. The tuned SVM with a threshold of 0.30 achieved higher delayed-class recall (0.857) and ROC-AUC (0.980). Feature-importance analysis indicated that fourth and fifth semester GPAs were the strongest predictors. These findings show that machine learning can support early academic intervention and data driven decision making in higher education.
Optimasi Pemilihan Indekos Dengan Menggunakan Metode TOPSIS Irvan Rahul Ramadika; Benny Daniawan
FORMAT Vol 15 No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2026.v15.i1.004

Abstract

Meningkatnya populasi Indonesia mendorong permintaan hunian layak yang semakin meningkat. Namun, memiliki rumah pribadi menjadi sulit karena tingginya suku bunga Kredit Pemilikan Rumah (KPR), yang seringkali tidak sesuai dengan pendapatan masyarakat. Hal ini menjadikan rumah kos sebagai alternatif yang menarik karena lebih terjangkau dan tidak memerlukan komitmen jangka panjang. Saat ini, pencarian dan pemasaran rumah kos masih mengandalkan metode konvensional, yang mengharuskan calon penyewa untuk datang langsung atau bergantung pada informasi masyarakat sekitar. Untuk mengatasi hal ini, penelitian ini bertujuan untuk merancang Sistem Pendukung Keputusan (SPK) berbasis web untuk memfasilitasi transaksi antara pemilik dan calon penyewa tanpa harus mengunjungi ataupun survei ke lokasi. Sistem ini menggunakan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) untuk memberikan rekomendasi rumah kos yang optimal. Kriteria yang dipertimbangkan meliputi Harga, Jarak, Fasilitas, Ukuran Kamar, dan Keamanan. Hasil TOPSIS menunjukkan rekomendasi terbaik adalah Artha Kos, dengan nilai preferensi tertinggi sebesar 0,7809. Hal ini karena rumah kos tersebut menawarkan kombinasi kriteria terbaik, seperti fasilitas dan keamanan, yang menunjukkan bahwa pengguna lebih mengutamakan kenyamanan dan keamanan daripada biaya ataupun jarak. Sistem ini juga mencakup fitur pemesanan langsung dan telah diuji menggunakan metode User Acceptance Testing (UAT), dengan tingkat kepuasan mencapai 86,4%. Hasil ini menunjukkan bahwa sistem ini efektif dan menyederhanakan pemesanan kos secara digital, memberikan solusi praktis untuk memenuhi kebutuhan perumahan di tengah tantangan kepemilikan rumah pribadi.
Co-Authors Abidin Abidin Abidin Abidin Aditiya Hermawan Amat Basri Andri Wijaya Ardiane Rossi Kurniawan Maranto ardie halim wijaya Ardie Wijaya Audrey Valencia Wijaya Darmayana Putra Dedi Wijaya Dellih Wijaya Desiyanna Lasut Deviastati Putri Sugiarta Karlim Devita Amalia Pertiwi Diana Juniar Edy Edy Eva Eva Fennita Fennita Fernaldi Makmur Gesima Angel Sirait Hebert Lou Wenson Irvan Rahul Ramadika Jacob Dethan Jacob Febryadi Nithanel Dethan Joese Nathaniel Junaedi Kevin Kurniawan Leona Fandini Luis Alberto Maichel Ruby Julianto Maysha Permata Putri Nanda Aristantiyo Meylinda Rainaldo Diogenes Susilo Ramona Safitri Rino Rino Riscal Arifin Rudy Arijanto Susanto Hariyanto Suwitno Suwitno Suwitno Tugiman Tugiman Vanni Andriana Vira Oktaviani Wijaya Wiyono Yakub Yuki Gunawan