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All Journal Infoman's: Jurnal Ilmu-Ilmu Manajeman dan Informatika TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi JPNM : Jurnal Pustaka Nusantara Multidisiplin Jurnal Penelitian Teknologi Informasi dan Sains Infomans: Jurnal Ilmu-ilmu Informatika dan Manajemen COSMOS Router : Jurnal Teknik Informatika dan Terapan Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika JIMT: Jurnal Informatika, Multimedia dan Teknik Router : Jurnal Teknik Informatika dan Terapan Indonesian Journal of Education (INJOE)
Asep Saeppani
Universitas Sebelas April
Religion Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Analisis Perbandingan Algoritma Regresi Linear dan Decision Tree untuk Prediksi Dropout Mahasiswa Abdah Syakiroh Gustian; Asep Saeppani
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 1 (2026): Januari : Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i1.1362

Abstract

This study aims to develop an effective predictive model for identifying students at risk of academic dropout using the Decision Tree and Linear Regression algorithms. The data used are sourced from the public Kaggle dataset Students Dropout and Academic Success, which includes demographic, socioeconomic, and academic performance variables for each semester. The research method includes data preprocessing stages, such as data cleaning, label encoding for categorical variables, numeric feature normalization, and target class adjustment to focus on binary classification, namely Dropout and Graduate. The modeling process is carried out by comparing the performance of the two algorithms using evaluation metrics of accuracy, precision, and recall. The results show that the Decision Tree algorithm has superior performance compared to Linear Regression in mapping non-linear patterns in student data. Feature importance analysis revealed that the number of curricular units in the second semester and tuition payment status are the main predictors of dropout risk. These findings are expected to assist educational institutions in implementing early interventions to improve student academic success.  
Analisis Sentimen Ulasan Google Play pada Aplikasi Tahu Sumedang Menggunakan Lexicon-Based Sidik, Khairil; Dody Herdiana; M. Agreindra Helmiawan; Asep Saeppani
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No2.pp356-361

Abstract

This study examines user perceptions of the Tahu Sumedang application based on reviews submitted on the Google Play Store. The purpose of this research is to identify user sentiment and understand the aspects that influence positive and negative evaluations of the application. The lexicon-based sentiment analysis method is used to classify each review into sentiment categories through a dictionary of words with positive and negative polarity. The results show variations in user sentiment that reflect satisfaction with certain features and concerns about technical performance, navigation, and application stability. The findings highlight several aspects that require improvement to optimize the quality of digital public services. The conclusion of this study emphasizes the importance of user feedback as an evaluation basis for developing more responsive and efficient public service applications.
OPTIMIZE TEXTILE BOOK RECOMMENDATION SYSTEM USING DEEP LEARNING ALGORITHMS Sitti Nur Alam; Asep Saeppani; Iwan Setiawan
Indonesian Journal of Education (INJOE) Vol. 3 No. 2 (2024): Indonesian Journal of Education (INJOE)
Publisher : CV. ADIBA AISHA AMIRA

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Abstract

The research aims to optimize the recommendation system for textile books by applying deep learning algorithms. The textile industry, rich in content and material variation, requires a system of recommendations that can accurately accommodate the diverse needs of its users. Deep learning, with its sophistication in processing large and complex data, offers solutions in improving the quality of recommendations. The study explores the use of deep learning models in interpreting user preferences and book characteristics, with the hope of producing more relevant and personal predictions. Research methods that literature conducts systematically through the collection of data from scientific sources such as journals, conferences, and related articles published in the last decade. The results show that deep learning algorithms such as Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN) have been successfully applied in improving the accuracy of book recommendation systems, including in textile contexts. These models are able to understand and process textile information and user preferences more deeply than traditional algorithms. The research also revealed important factors that influence model performance, such as data quantity and quality, model architecture, and parameter setting. Although there are limitations associated with resource use and the need for large datasets, the use of deep learning algorithms in recommendation systems for textile books shows significant potential in improving personalization and user satisfaction.
Co-Authors Abdah Syakiroh Gustian Al-Amin, Al-Amin Alam, Sitti Nur Dani Indra Junaedi Dipa Arya Pangestu Dody Herdiana Fidi Supriadi Guntara, Agun Indriyani Surachman, Revaliana Irfan Fadil Iwan Setiawan Iwan Setiawan M.Arif Firmansyah Mahardika, Fathoni Muhammad Agreindra Helmiawan Muhammad Arifin Ilham Nugraha, Rahmat Taufik Puspanagara, Amanda Listiana Restuyadi, Andrian Rizky Rizki, Rifky Septiana Sidik, Khairil Sitti Nur Alam Sofiyan, Yanyan