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All Journal Jurnal Simetris Rekursif: Jurnal Informatika Jurnal Pseudocode Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) Jurnal Informatika JSAI (Journal Scientific and Applied Informatics) Abdi Reksa
Desi Andreswari
Universitas Bengkulu
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Implementasi Metode Topsis Pada Sistem Pendukung Keputusan Penentuan Prioritas Penerima Bantuan Mesin Untuk Sentra Ikm Berbasis Website (Studi Kasus : Dinas Perindustrian Dan Perdagangan Provinsi Bengkulu) Exca Wella Monica; Desi Andreswari; Julia Purnama Sari
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.82-88

Abstract

The industrial sector is one of the economic sectors being developed in Indonesia as a driving force for the progress of other economic sectors. One form of this is the Small and Medium Industries (SMEs), which are quite dependent on government assistance, both in terms of capital and equipment. One of the government programs to advance SMEs is the provision of machinery assistance to SME centers. In practice, the selection process for machine assistance recipients at the Bengkulu Industry and Trade Office still uses Microsoft Excel, which has limitations, including the risk of human error, logical errors in formulas, differences in results due to rounding numbers, and a lack of objectivity in assessments. Therefore, a Decision Support System (DSS) is needed to minimize errors, producing more objective, transparent, and accurate decisions. This study uses the TOPSIS method to calculate the preference value of SMEs and data aggregation techniques (averages) to calculate the final value of SME centers. The resulting output is a ranking of SME centers eligible for machine assistance. Of the total of 26 comparisons between the rankings from the system and the rankings from the Industry and Trade Office, 20 rankings were obtained that were the same, so that the system accuracy reached 76.92%. Furthermore, the system's feasibility test achieved an interval score of 4.6, or 92%, which is considered very good. The final result of this research is the creation of a website-based decision support system for prioritizing recipients of machine assistance for small and medium enterprises SME centers.
Motіon graрhісs Motіon graрhісs рada medіa рembelajaran self resсue berbasіs androіd untuk anak tunagrahіta rіngan hingga sedang рada materі mengamankan dіrі darі benda-benda berbahaуa (Studi Kasus: SLB Negeri 1 Kota Bengkulu): (Studi Kasus: SLB Negeri 1 Kota Bengkulu) Ejiman Saputra; Desi Andreswari; Widhia KZ Oktoeberza
Jurnal Pseudocode Vol 13 No 2 (2026): Volume 13 Nomor 2 September 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.2.124-130

Abstract

Children with mild to moderate intellectual disabilities experience cognitive constraints that limit their ability to identify, assess, and avoid hazardous objects in their surrounding environment, thereby increasing their vulnerability to accidents both in school settings and everyday activities. This condition highlights the urgent need for learning media that are not only safe and developmentally appropriate but also engaging and visually supportive to strengthen self-rescue competencies. This study aims to develop and evaluate the effectiveness of an Android-based self-rescue learning medium incorporating motion graphics animation focused on self-protection from dangerous objects. The research adopted a Research and Development (R&D) approach using the Multimedia Development Life Cycle (MDLC) model. The participants consisted of 28 phase D students with mild to moderate intellectual disabilities at SLB Negeri 1 Bengkulu City. Application effectiveness was measured through pre-test and post-test scores analyzed using the Wilcoxon signed-rank test. The findings revealed a statistically significant improvement in students’ understanding after the intervention, with a significance value of 0.01 (<0.05). These results confirm that the developed media effectively enhances students’ ability to recognize and protect themselves from sharp, pointed, slippery, and hot objects. Keywords: Android; Dangerous Objects; Mild Intellectual Disability; Motion Graphics; Self-Rescue.
Implementasi Metode Adaptif Neuro-Fuzzy Inference System (ANFIS) Dalam Sistem Pakar Prediksi Risiko Diabetes Mellitus Tipe II Ratna Yanti Simbolon; Desi Andreswari; Julia Purnama Sari; Ester Morina Silalahi
Jurnal Pseudocode Vol 13 No 2 (2026): Volume 13 Nomor 2 September 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.2.131-141

Abstract

Type 2 Diabetes Mellitus (T2DM) is frequently undetected at an early stage because its symptoms develop gradually. Therefore, an accessible risk-screening method is needed to support early awareness and further medical examination. This study developed an Adaptive Neuro-Fuzzy Inference System with a Takagi-Sugeno-Kang structure for three-class T2DM risk prediction using eight non-laboratory health indicators from the Behavioral Risk Factor Surveillance System dataset. After conflict removal, 65,369 records were divided into stratified training, validation, and testing sets. SMOTENC was applied only to the training set to reduce class imbalance. To provide a fair evaluation, ANFIS was compared with Logistic Regression, Decision Tree, Random Forest, XGBoost, Linear Support Vector Machine, Multilayer Perceptron, and a majority-class predictor under the same experimental protocol. Macro-F1 was used as the primary evaluation metric because of the severe class imbalance. A sensitivity analysis of Top-K values of 128, 256, and 512 were also conducted, and permutation importance was used to examine the contribution of each input variable. The final ANFIS model using Top-K = 128 achieved an accuracy of 84.18%, a Macro-F1 of 48.59%, a weighted F1-score of 88.06%, and a balanced accuracy of 62.85%. Random Forest achieved the highest Macro-F1 of 50.98%, indicating that ANFIS did not outperform the strongest baseline in overall class-balanced performance. However, ANFIS achieved the highest balanced accuracy and the highest prediabetes recall among the evaluated models. BMI, GenHlth, and Age were the most influential variables according to permutation importance. The selected model was implemented in a Django-based web expert system as a preliminary risk-screening prototype rather than a medical diagnostic tool. Keywords: ANFIS, Type 2 Diabetes Mellitus, Expert System, Risk Prediction
Co-Authors Aan Erlansari Aan Erlanshari Ahmad Fauzi Ajei Ibnu Rahmat Amri Hamdani Andiran Mukhtadin Asahar Johar Avindho Fattah Akbar Bambang Gonggo Murcitro Boko Susilo Edo Koriska Ejiman Saputra Endina Putri Purwandari Ernawati Ernawati Ester Morina Silalahi Exca Wella Monica Febridila Nurul Masyita Fenty Oktavia Funny Farady C funny farady Coastera iffan 29 Irvan Alfajri Julia Purnama Sari Krisna Prastio Kurnia Anggriani, Kurnia Magdalena Makarios Adonia Sigalingging Mochammad Yusa Muhammad Iqbal Baskoro Muhammad Naufal Faruqhy Nanda Satriawan Neferly Arif Budiman Nella Astiani Novia Bella Vista Nur Aini Nur Annisa Apriliani Herman Putri Nur Faizah Putri Rahayu Randy Viyata Dhika Ratna Yanti Simbolon Ricky Sadewa Rizka Yulia Ningsih Rusdi Efendi Saleha, Nurmukaromatis Sani Putriana Sayyid Taqial Haidar Selfi Ristiarini Nasution Sempurna Ginting Setiawan, Yudi Sonia Rosalin Sihite Syifa Mawaddah Syofian Irwanda Tetes Wahyu Witradharma Tiara Elsi Febrianti Tommy Alexander Ulva Faraditha Utama, Ferzha Putra Vinta Asmika Vonny Afriyona Egisthi Widhia KZ Oktoeberza