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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Ilmu dan Teknologi Kesehatan Computer Science (CO-SCIENCE) Jurnal Pengabdian Masyarakat
Andy Maulana Yusuf
Telkom University
Computer Science & IT Education Environmental Science Health Professions Social Sciences Other

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IMPLEMENTASI GOOGLE SPREADSHEET DAN APPS SCRIPT DALAM SISTEM PENJADWALAN SIDANG TUGAS AKHIR PADA JURUSAN ENERGI DI POLITEKNIK XYZ Musrinah; Andy Maulana Yusuf
BESIRU : Jurnal Pengabdian Masyarakat Vol. 3 No. 5 (2026): BESIRU : Jurnal Pengabdian Masyarakat, Mei 2026
Publisher : Lembaga Pendidikan dan Penelitian Manggala Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62335/besiru.v3i5.2552

Abstract

Departement of Energy of XYZ Polytechnic faces efficiency challenges in managing the final project trial schedule which is still conventional, so it is prone to schedule conflicts. This community service activity aims to implement a cloud-based scheduling information system using Spreadsheet and Apps Script to automate data validation. The implementation method includes partner needs analysis, database design, development of an 8-second filter logic script, and functional trials. The results of the activity show that this system is able to eliminate the potential for human error through automatic elimination features on the lecturer selection list and early warning notifications. This implementation has succeeded in significantly increasing the efficiency of scheduling time, from the original three days to only two hours. This system ensures data accuracy and facilitates transparent and real-time coordination between the final project coordinating lecturers involved.
Pendekatan Hibrida menggunakan Sistem Inferensi Fuzzy dan Pembelajaran Mendalam untuk Klasifikasi Penyakit Alzheimer pada Citra MRI Bagas Wibowo; Andy Maulana Yusuf; Bintang Vieshe Mone; Sabrina Adinda Sari
jitek Vol 13 No 2 (2026): Maret 2026
Publisher : Poltekkes Kemenkes Jakarta III

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32668/jitek.v13i2.2381

Abstract

Early detection of Alzheimer's disease using brain MRI image data can substantially improve clinical intervention and patient management. Our study evaluates the performance of an Alzheimer's classification system based on Fuzzy Inference Systems (FIS), specifically for the Mamdani and Sugeno models, in identifying four patient categories: (1) Non-Dementia, (2) Very Mild Dementia, (3) Mild Dementia, and (4) Moderate Dementia. In addition, this study compares the classification performance and computational efficiency of several deep learning architectures, including a traditional CNN (VGG16), a modern model (EfficientNet-B0), and a hybrid Fuzzy Convolutional Inference Engine (FCIE) that integrates CNN-based feature extraction with fuzzy logic reasoning. The dataset used consists of normalized and augmented Alzheimer's MRI images, and each model was trained and validated using a 70%:15%:15% split for training, validation, and testing. Experimental results show that the Mamdani and Sugeno FIS models achieve validation accuracies of about 32% and 35%, respectively, which highlights the limitations of pure texture-based features in capturing complex classification patterns. In contrast, VGG16 and EfficientNet-B0 produced validation accuracies of 82.81% and 85.22%, respectively, with AUC values of 0.95 and 0.96, respectively. However, when both schemes were combined into a hybrid model FCIE achieved the highest validation accuracy of 98.03% and AUC of 0.99. Comparative analysis of metrics, including precision, recall, F1-score, AUC, and training duration, shows a clear trade-off between accuracy and computational efficiency. This study recommends the FCIE model for clinical applications requiring high diagnostic accuracy, while EfficientNet-B0 is suggested for medical environments with moderate GPU resource constraints.
Integrating LLM Intent-Aware Approach for Enhancing the Quality of Bundle Recommendations Andy Maulana Yusuf; Musrinah Musrinah
Computer Science (CO-SCIENCE) Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i2.12101

Abstract

Digital grocery shopping has shifted consumer patterns toward multi- item purchasing. While bundle recommendation systems address this, existing models relying on product ID co-occurrence fail to capture the mixed shopping intentions inherent in consumer baskets. To address these limitations, we propose Intent-aware Bundle Recommendation (IABR), a framework shifting from structural matching to semantic intent reasoning. IABR utilizes Large Language Models to decompose baskets into coherent sub-packages. Subsequently, we fine- tuned Gemma-3-4B using Parameter-Efficient Fine-Tuning to generate narrative intent descriptions regarding short-term shopping missions and long-term sustainable user lifestyles. These intents are encoded via Sentence Transformers for semantic retrieval. Extensive testing on the Instacart dataset demonstrates IABR’s significance against baselines like BGCN. Our IABR method achieved a Recall@20 of 28.15% while improving diversity scores by 12% (p < 0.05). This validates that generative semantic modeling enables accurate next- bundle predictions, effectively balancing precision with thematic variation and personalization.
IMPROVING LONG-TAIL RECOMMENDATION VIA ROULETTE WHEEL SELECTION USING HYBRID RATING AND TEMPORAL DECAY WEIGHTING Andy Maulana Yusuf; Miftah Farid Adiwisastra; Faizal Riza; Musrinah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8240

Abstract

The phenomenon of information overload on digital platforms frequently leads to a skewed long-tail distribution, where user interactions concentrate on popular items while the majority remains undiscovered. This popularity bias is particularly acute in the book domain, where metaphorical titles defy traditional lexical matching, causing unique content to remain hidden in the long-tail area. While semantic models improve relevance, a significant research gap persists in mitigating the trade-off between accuracy and catalog exposure. This study proposes a novel stochastic framework by integrating Roulette Wheel Selection with hybrid rating and temporal decay weighting to enhance long-tail discoverability. To evaluate its generalizability, this mechanism was tested on the Goodreads Poetry dataset across four distinct content-based embedding models: SBERT-Tuned, SBERT, FastText, and Word2Vec. We conducted a rigorous comparative analysis between the proposed Stochastic (S) approach and Deterministic (D) Top-N selection. Experimental results demonstrate that the stochastic mechanism consistently improves catalog diversity across all baselines. SBERT-Tuned emerged as the most promising model, achieving a 461.71% increase in Catalog Coverage with a marginal 2.15% Recall reduction. Other models also exhibited significant gains, where the integration of Roulette Wheel Selection boosted coverage by 381.72% for FastText and 225.15% for Word2Vec, highlighting the framework's robustness. Furthermore, a consistent decrease in the Gini-index and an improvement in Novelty scores (ranging from +16% to +29%) confirm a more equitable distribution of item visibility. This research contributes a methodological advancement by proving that stochastic selection, guided by time-aware weighting, can effectively dismantle popularity bias without sacrificing relevance.
Co-Authors Bagas Wibowo Bintang Vieshe Mone Faizal Riza Miftah Farid Adiwisastra Musrinah Musrinah Musrinah Musrinah Sabrina Adinda Sari