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Rozi Meri
Politeknik Negeri Padang
Computer Science & IT

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Implementasi Metode TOPSIS Pada Sistem Pendukung Keputusan Pemilihan Depot Air Minum Isi Ulang Dian Permata Sari; Rozi Meri; Wira Buana; Mike Febri Mayang Sari
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1843

Abstract

Air adalah kebutuhan dasar bagi semua makhluk hidup, dan keberadaannya sangat penting untuk kelangsungan hidup. Seiring dengan meningkatnya permintaan akan air, ketersediaan air bersih tidak selalu sebanding, sehingga banyak masyarakat yang memilih air minum isi ulang yang praktis dan terjangkau. Masyarakat diharapkan bijak dalam memilih depot air minum yang higienis dan sehat sesuai dengan standar yang ditetapkan oleh pemerintah. Metode TOPSIS, yang merupakan bagian dari Sistem Pendukung Keputusan, dapat membantu dalam memilih depot air minum isi ulang yang sehat. TOPSIS adalah metode pengambilan keputusan multikriteria yang menggunakan prinsip bahwa alternatif yang dipilih harus memiliki jarak terdekat dengan solusi ideal positif dan jarak terjauh dari solusi ideal negatif. Ada empat variabel yang digunakan untuk memilih depot air minum isi ulang yang sehat, yaitu kualitas air, sanitasi penjamah, peralatan, dan kelengkapan. Hasil analisis dengan metode TOPSIS memberikan rekomendasi depot air minum yang sehat. Berdasarkan perhitungan nilai preferensi, alternatif V1 memperoleh nilai tertinggi sebesar 0.87188, diikuti oleh alternatif V13 dengan nilai 0.84037, dan seterusnya. Dengan demikian, alternatif V1 direkomendasikan sebagai depot air minum isi ulang yang terbaik. Sistem pendukung keputusan dengan metode TOPSIS dapat dijadikan panduan dalam pemilihan depot air minum isi ulang.
Quantifying Target Leakage in Rule-Derived Mental Health Screening Labels Rozi Meri; Ikhsan; Dian Eka Putra; Sofia Yosse; Ismael
Journal of Systems Engineering and Information Technology (JOSEIT) Vol. 3 No. 3 (2024)
Publisher : Ikatan Ahli Informatika Indonesia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v3i3.8470

Abstract

Publicly hosted tabular datasets increasingly package screening-questionnaire items alongside computed, not clinically observed, risk and severity labels. This creates a risk of target leakage: if a label is a deterministic function of the same features given to a classifier, reported predictive performance measures rule reconstruction, not screening validity. We audit a recent IEEE DataPort dataset (n = 2,005) scoring 27 Likert items across five conditions – ADHD, Autism Spectrum Disorder (ASD), Social Pragmatic Communication Disorder (SPCD), Depression, and Anxiety – each with a binary risk flag and a three-level severity label. The dataset's documentation discloses that every label is generated from a threshold or tertile rule on the summed items of its own condition, yet its usage instructions recommend fitting Random Forest or XGBoost with SHAP directly on the 27 raw items. We verify the documented rule empirically, then compare a trivial single-feature decision stump (the summed score) against a 300-tree Random Forest trained on all 27 items, under identical 5-fold stratified cross-validation, across all ten condition-by-task combinations (5 conditions × {risk, severity}). The single-feature stump matches or exceeds the 27-feature ensemble in every combination, reaching 100% cross-validated accuracy on nine of ten targets. Random Forest feature importance further shows 72–88% of predictive weight concentrated on each condition's own items. These results indicate the pipeline the dataset recommends reconstructs a disclosed arithmetic rule rather than learning a clinically meaningful pattern; any predictive-validity study on this dataset should exclude a condition's own summed items from that condition's feature set.
Addressing Data Leakage and Reproducibility Crises in Machine-Learning-Based Biomechanics Martina Enyanwu Okoh; Amina Ojochide; Riyan Ikhbal Salam; Novi Efendi; Rozi Meri
Journal of Systems Engineering and Information Technology (JOSEIT) Vol. 4 No. 3 (2025)
Publisher : Ikatan Ahli Informatika Indonesia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/z4sxww09

Abstract

Gait-event features dataset intended to support machine-learning-based locomotion analysis, but delivered files are rarely checked against their own documentation or against standard values from the gait-analysis literature. We apply a six-stage, three-layer audit (provenance, structural/physical plausibility, signal validity) to “Robust Gait Event Detection Using GRF and EMG Signals,” a 208-row, 12-feature dataset describing four stance-phase gait events (Heel Strike, Loading Response, Mid-Stance, Heel Off) from 52 participants. Row count, feature count, and participant count all match the hosting page’s Instructions section. However, direct inspection finds two of the twelve documented features are exact duplicates under different names: the column labeled COV (coefficient of variation) is byte-identical to VAR (variance) rather than the standard STD/mean ratio, and the column labeled mode is byte-identical to minimum across all 208 rows, consistent with a computation error rather than genuine central-tendency estimation. An eight-row contiguous block is duplicated verbatim at a fixed 32-row offset, indicating the true unique sample is 50 participants (200 rows), not the documented 52 (208 rows). The file also provides no field distinguishing GRF-derived from EMG-derived features, despite both being named as contributing sources in the dataset’s title and abstract. Finally, while all twelve features discriminate strongly between the four event classes by one-way ANOVA, the percentage feature is exactly 100.000000 with zero variance for every Heel-Off row, consistent with a windowing artifact of the class definition rather than an independently varying measurement, and its per-class averages are inconsistent with standard stance-phase timing reported in the clinical gait-analysis literature.
Co-Authors Amina Ojochide Dian Permata Sari Eka Putra, Dian Ikhbal Salam, Riyan Ikhsan Ismael Martina Enyanwu Okoh Mike Febri Mayang Sari Novi Efendi Sofia Yosse Wira Buana