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All Journal Jurnal Ilmiah Teknologi dan Rekayasa Informatics and Software Engineering (ISE) Journal of Decision Support Systems and Multi-Criteria Decision Making (JODESMA)
Sandi Badiwibowo Atim
Lampung University
Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Klasifikasi tingkat stres berbasis fuzzy mamdani menggunakan indikator aktivitas harian Sandi Badiwibowo Atim; Erin Eka Citra
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 31 No. 1 (2026)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2026.v31i1.180

Abstract

Peningkatan beban kerja dan penurunan kualitas tidur dalam pola hidup modern telah memicu peningkatan tingkat stres individu, yang sering kali dinilai secara subjektif dan kurang akurat menggunakan pendekatan konvensional. Penelitian ini mengusulkan model klasifikasi tingkat stres berbasis Fuzzy Inference System (FIS) Mamdani untuk menangani ketidakpastian dan subjektivitas pada indikator gaya hidup. Model ini menggunakan empat variabel input: sleep duration, sleep quality, physical activity level, dan daily steps, dengan tingkat stres sebagai variabel output. Data penelitian bersumber dari sleep health and lifestyle dataset sebanyak 374 data. Sistem dirancang menggunakan 20 aturan fuzzy dan metode defuzzifikasi centroid yang diimplementasikan melalui aplikasi berbasis web interaktif. Hasil pengujian menunjukkan performa model yang sangat baik dengan nilai Root Mean Square Error (RMSE) sebesar 1,671 dengan akurasi sebesar 88,50% dan Mean Squared Error (MSE) sebesar 1,671. Hasil Ini menunjukkan bahwa logika fuzzy efektif dalam memberikan hasil klasifikasi yang stabil dan memiliki interpretabilitas tinggi dalam merepresentasikan kondisi manusia.
Tier-Aware Entropy-ARAS Approach to Select Microcontroller Boards for Education Sanriomi Sintaro; Sandi Badiwibowo Atim; Vederico Pitsalitz Sabandar
Informatics and Software Engineering Vol. 3 No. 2 (2025): December
Publisher : SAN Scientific

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58777/ise.v3i2.574

Abstract

This study develops a decision support system to recommend microcontroller and IoT learning devices for schools, universities, and training centers under realistic budget constraints while considering both technical capability and educational suitability. The alternatives are grouped into three budget tiers and evaluated using nine criteria covering price, CPU frequency, Flash, RAM/PSRAM, connectivity, usable GPIO, ease of learning, learning resources/community, and local availability/warranty. Objective criterion weights are computed using the Entropy method, and tier-wise rankings are produced using Additive Ratio Assessment (ARAS) through utility scores relative to an ideal alternative. Indicative local price and availability information are compiled from Tokopedia, while qualitative criteria are scored using consistent rubrics to support reproducibility. The results identify ESP32-CAM + baseboard as the top recommendation in Tier 1, LILYGO T-Display S3 in Tier 2, and M5StickC Plus2 in Tier 3; across tiers, Entropy assigns the largest weights to the most discriminative criteria, particularly RAM/PSRAM and, in higher tiers, Flash. The study is limited by market price volatility, approximations in usable GPIO values, and rubric-based qualitative scoring, and it also reflects the tendency of Entropy to concentrate weights on highly dispersed criteria, potentially amplifying outlier advantages. Overall, the proposed tier-aware Entropy–ARAS framework provides a transparent and actionable approach for educational institutions to justify device procurement and usage decisions based on budget, functionality, and learning readiness.
Logarithmic Normalization and Standard Deviation Modification for Enhanced Sensitivity in Objective Criteria Weighting Vederico Pitsalitz Sabandar; Yulian Hany Makaruku; M. Ikbal Siami; Sandi Badiwibowo Atim; Sanriomi Sintaro
Journal of Decision Support Systems and Multi-Criteria Decision Making Vol. 1 No. 3 (2027): July 2027
Publisher : Asosiasi Peneliti Informatika dan Komputer untuk Riset (PILAR)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67449/jodesma.v1i3.15

Abstract

Objective weighting methods are essential in multi-criteria decision-making (MCDM) because criterion weights directly influence alternative rankings. However, dispersion-based weighting approaches may assign excessive importance to criteria with high variability, resulting in an imbalanced weighting structure. This study proposes LOGSTA-D, an extension of the Logarithmic Normalization and Standard Deviation (LOGSTA) method that introduces a Dispersion Compression Transformation based on the square root of standard deviation. The transformation is designed to moderate differences in criterion dispersion before logarithmic dispersion is used to derive the final criterion weights. The proposed method is evaluated using a supplier selection dataset consisting of nine alternatives and eight criteria and is integrated with four MCDM methods, namely SAW, MOORA, WP, and WASPAS. The results show that LOGSTA-D produces a more moderated distribution of criterion weights by reducing the dominance of highly dispersed criteria and redistributing importance toward criteria with lower initial weights. Across the evaluated MCDM methods, the resulting rankings exhibit a high degree of consistency, with limited changes among intermediate alternatives. The robustness analysis shows that the LOGSTA-D with MCDM framework achieves a robustness value of 0.9875, compared with 0.9604 for the LOGSTA with MCDM framework, indicating a higher level of ranking consistency under the evaluated conditions. These findings demonstrate that LOGSTA-D provides a controlled dispersion-based weighting mechanism that can support robust decision-making in supplier selection problems.
Co-Authors Erin Eka Citra M. Ikbal Siami Sanriomi Sintaro Vederico Pitsalitz Sabandar Vederico Pitsalitz Sabandar Yulian Hany Makaruku