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All Journal Jurnal Teknologi dan Informasi INTI Nusa Mandiri Simpatik: Jurnal sistem Informasi dan Informatika Jurnal Informatika dan Multimedia TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Infomans: Jurnal Ilmu-ilmu Informatika dan Manajemen Jurnal Informatika: Jurnal Pengembangan IT Riau Jurnal Teknik Informatika Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Indonesian Journal of Education (INJOE)
Fathoni Mahardika
Universitas Sebelas April
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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School Website Design for SLB ABC Muhammadiyah Sumedang Using the Design Thinking Method Rayhan Fauzan Wahyu Hidayat; Fathoni Mahardika; Deris Santika
Infoman's : Jurnal Ilmu-ilmu Informatika dan Manajemen Vol. 20 No. 1 (2026): Infoman's
Publisher : LPPM & Fakultas Teknologi Informasi UNSAP

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

Abstract

The development of information technology requires educational institutions to adapt to the digital era, including in the dissemination of information to stakeholders. SLB ABC Muhammadiyah Sumedang still relies on conventional methods such as bulletin boards and WhatsApp in disseminating school information, which are considered ineffective and not well documented. This research aims to design a user-centered school website design using the design thinking method in order to meet the needs of digitizing school information optimally. The research method used is design thinking with five main stages: empathize, define, ideate, prototype, and test. Data collection was carried out through in-depth interviews with teachers and school staff as the main users. The data is then analyzed using affinity diagrams, user personas, user journey maps, pain points analysis, and priority matrix to identify the needs and priorities of feature development. The prototype website was designed using Figma with four main pages: Home, Profile, Extracurricular, and Gallery. The prototype evaluation was carried out using the System Usability Scale (SUS) and User Experience Questionnaire (UEQ) instruments involving 20 respondents. The results of the study show that the website prototype successfully meets the needs of users very well. The SUS evaluation showed an average score of 74 in the "Good to Excellent" category, while the UEQ evaluation resulted in an average score of 1.98 with an interpretation of "Excellent" that was in the top 10% of results based on international benchmarks. The resulting website design has clear navigation, responsive display, and structured content according to the needs of teachers and staff in managing school information. This research proves that the design thinking approach is effective in producing school website designs that are truly based on user needs. The iterative process in design thinking ensures that every feature and appearance of the website is developed based on empirical data from real users, resulting in a human-centered digital solution that can improve the effectiveness of communication and documentation of activities in the educational environment.
Perbandingan Xgboost dan Logistic Regression dalam Memprediksi Credit Card Customer Churn Rearizth Muhammad Daffaa; Deris Santika; Fathoni Mahardika
Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Vol. 3 No. 3 (2025): Mei : Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupiter.v3i3.807

Abstract

With the times, cash transactions that used to use cash are now turning to credit cards. However, the increasing use of credit cards presents challenges, especially in maintaining customer loyalty. Customer churn is the loss of customers within a certain period for various reasons. Logistic Regression is a machine learning algorithm that studies the relationship between a dependent variable and several independent variables and Extreme Gradient Boosting (XGBoost) is a Gradient tree-boosting algorithm that offers out-of-core learning and sparsity awareness.The purpose of this study is to compare the performance between Logistic Regression and Extreme Gradient Boosting (XGBoost) algorithms in predicting customer churn in credit card services using evaluation metrics such as accuracy, precision, recall, and F1-score. Based on the research results, it can be concluded that XGBoost has better performance in all evaluation metrics, both in terms of precision, recall, F1-score, and accuracy. Based on the research, XGBoost shows superior performance compared to Logistic Regression in all evaluation metrics.
Analisis Klasifikasi Risiko Dropout Mahasiswa Menggunakan Algoritma Decision Tree dan Random Forest Abdah Syakiroh Gustian; Fathoni Mahardika
Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Vol. 3 No. 4 (2025): Juli: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupiter.v3i4.980

Abstract

This study aims to develop an accurate predictive model for identifying students at risk of academic dropout using Decision Tree and Random Forest algorithms. The research utilizes a publicly available dataset sourced from Kaggle, which includes academic and demographic features such as GPA, attendance, credit load, financial aid status, and exam scores. The methodology involves several stages: data collection, preprocessing (handling missing values, encoding categorical variables, and feature scaling), model training, and evaluation using performance metrics such as Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. Results show that the Random Forest algorithm outperforms Decision Tree in terms of accuracy and robustness, with notable feature importance on math, reading, and writing scores. The findings highlight the potential of machine learning in early detection of dropout risks and provide actionable insights for academic institutions to design timely interventions. This research contributes to the growing field of educational data mining and supports data-driven decision-making processes in higher education management.
Co-Authors Abdah Syakiroh Gustian Agun Guntara Anisa Pebriyani Huslan Asep Saeppani CYNTHIA PETRA HAUMAHU Dani Indra Junaedi Deden Adi Mardian Lesmana Deden Hidayat Deris Santika Fidi Supriadi Fina Nur'aeni Muhamad Rian Aprilyawan Nida Shofwatunnisa Purnama, Yulian Rahmat Taufik Nugraha Rayhan Fauzan Wahyu Hidayat Rearizth Muhammad Daffaa Revaliana Indriyani Surachman