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All Journal Jurnal Sains Komputer dan Teknologi Informasi JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Sistim Informasi dan Teknologi
Jusmawati
Universitas Yapis Papua
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Analisis PIECES terhadap E-Commerce Produk Daur Ulang pada Bank Sampah Jayapura Muhammad Taher Jufri; Jusmawati; Kartini Darma Waromi
Jurnal Sistim Informasi dan Teknologi 2022, Vol. 4, No. 2
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (464.443 KB) | DOI: 10.37034/jsisfotek.v4i2.131

Abstract

The system in the process of marketing transactions, sales and purchases of recycled products made by women is centered at the Jayapura Waste Bank. This centralized system has the effect of being less well known by the public. This system is also still weak in promotion, either through print or social media. Promotion only takes place from customers who have purchased products at this bank. In addition, there is no means of purchasing and paying online. So this research was conducted with the aim of building an online system by applying the waterfall method, data collection method, PIECES analysis method, UML design method and black box testing method. The result of this research is an online system in the form of e-Commerce Recycled Products at the Jayapura Waste Bank. These results make it easier to get reliable mobile-based marketing, sales, and purchase transaction process for recycled products. So that this system can be used as a reference in building a waste bank.
SISTEM INFORMASI MANAJEMEN MASJID ALMU’MINUN MAPOLDA PAPUA PADA KOTA JAYAPURA Siti Nurhayati; Muhammad Taher Jufri; Andrian Sah; Mursalim Tonggiroh; Jusmawati; Imamul Hakim
Jurnal Sains Komputer dan Teknologi Informasi Vol. 5 No. 2 (2023): Jurnal Sains Komputer dan Teknologi Informasi
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pengurus Masjid Al-Mu’minun Mapolda Papua melakukan manajemen seluruh aktivitas yang ada di dalam masjid yang terdiri dari: manajemen surat masuk dan surat keluar, manajemen aset masuk dan aset keluar, manajemen peminjaman aset oleh sekretaris, manajemen kas masuk dan kas keluar oleh bendahara, dan manajemen penerimaan zakat dan penyaluran zakat oleh amil zakat. Namun terdapat beberapa kekurangan dalam manajemen tersebut seperti beberapa manajemen belum ada pencatatan atau rekapitulasi yang digunakan dalam membuat laporan pertanggungjawaban pengurus masjid. Maka dari itu perlu adanya sebuah sistem yang dapat melakukan manajemen seluruh aktivitas masjid. Pengembangan Sistem Informasi Manajemen Masjid Al-Mu’minun Mapolda Papua menggunakan metode Rapid Application Development (RAD) sebagai alur pengembangan sistem. Dengan Entity Relationship Diagram (ERD), Flowmap, Flowchart, dan Unified Modelling Language (UML) sebagai tools dalam analisis maupun perancangannya. Dalam pengembangan sistem menggunakan PHP dan HTML sebagai bahasa pemrograman, Bootstrap sebagai framework, dan MySQL sebagai database. Metode pengujian yang digunakan yaitu metode pendekatan black box testing. Dari penelitian ini dihasilkan Sistem Informasi Manajemen Masjid Al-Mu’minun Mapolda Papua yang digunakan oleh pengurus masjid dalam memanajemen seluruh aktivitas masjid.
Model Klasifikasi Diabetes Menggunakan XGBoost Dengan Optimasi Seleksi Fitur Dan Hyperparameter Berbasis PSO Sheila putri aprilianti; Andrian Sah; Siti Nurhayati; Rasna; Jusmawati
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 7 No 2 (2026)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.7.2.620

Abstract

The rising global burden of diabetes mellitus has increased the need for accurate, technology-based early detection systems. This study develops a diabetes classification model using Extreme Gradient Boosting (XGBoost) optimized through a two-stage Particle Swarm Optimization (PSO) scheme: Binary PSO (BPSO) for feature selection and Global Best PSO (GBPSO) for hyperparameter tuning. Data were obtained from the Kaggle Diabetes Prediction Dataset (100,000 records; eight clinical attributes: gender, age, hypertension, heart disease, smoking history, BMI, HbA1c level, and blood glucose level). The extreme class imbalance (91.5% normal vs 8.5% diabetes) was addressed using the SMOTETomek hybrid technique. BPSO retained all eight features as the optimal combination (best cost 0.0329; F1-weighted 96.71%), while GBPSO produced the best hyperparameter configuration (n_estimators=416, learning_rate=0.237, max_depth=3, min_child_weight=3; best cost 0.0308, converging at the 11th iteration). The final model achieved 97.15% test-set accuracy, a ROC-AUC of 0.9779, and a diabetes-class precision of 0.93. The model was deployed as a Streamlit-based web system classifying patients into three risk categories: Not Indicated, Early Risk Indicated, and Diabetes Indicated. Preliminary validation on five real patient records from an anonymized partner hospital in Jayapura City showed classification results fully consistent with patients' clinical status (5 of 5 correct), indicating potential clinical applicability, although larger-scale testing is still required. These findings demonstrate that integrating XGBoost with a two-stage PSO optimization scheme produces an accurate and clinically applicable diabetes classification model.
Co-Authors Andrian Sah Imamul Hakim Kartini Darma Waromi Muhamad Taher Jufri Muhammad Ali Nur Hidayat Mursalim Tonggiroh Rasna Sheila putri aprilianti Siti Nurhayati