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All Journal REKAYASA Jurnal Informatika dan Teknik Elektro Terapan Jurnal Sisfokom (Sistem Informasi dan Komputer) SENTIA 2016 Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Jurnal Informatika Kaputama (JIK) Scientific Journal of Informatics Journal of Computer System and Informatics (JoSYC) Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi SKANIKA: Sistem Komputer dan Teknik Informatika Journal of Electrical Engineering and Computer (JEECOM) International Journal of Computer and Information System (IJCIS) Indexia Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Insand Comtech : Information Science and Computer Technology Journal JUSTIFY : Jurnal Sistem Informasi Ibrahimy COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Nusantara Journal of Computers and its Applications SINAR: Sinergi Pengabdian dan Inovasi untuk Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Abu Tholib
Universitas Nurul Jadid
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Physics Public Health Transportation Other

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Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms Abu Tholib; M Noer Fadli Hidayat; Supri yono; Resty Wulanningrum; Erna Daniati
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 2 No. 2 (2023): September 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i2.3364

Abstract

Student graduation is a very important element for universities because it relates to college accreditation assessment. One of them is at the Faculty of Engineering Nurul Jadid University, which has problems completing the study period within a predetermined time. So that it can be detrimental because accreditation is less than optimal, and the number of active students makes it less ideal in teaching and learning activities. This study aimed to compare the level of accuracy using the C4.5 algorithm and Naïve Bayes method in predicting graduation on time. The C4.5 and Naïve Bayes algorithms are one of the methods in the algorithm for classifying. Tests were carried out using the C4.5 and Naïve Bayes algorithms using Google Colab with Python programming language, then validated using 10-fold cross-validation. The results of this study indicate that the Naïve Bayes method has a higher accuracy value with an accuracy rate of 96.12%, while the C4.5 algorithm method is 93.82%.
Penerapan Machine Learning untuk Penentuan Mata kuliah Pilihan pada Program Studi Informatika Fathorazi Nur Fajri; Abu Tholib; Wiwin Yuliana
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.3990

Abstract

Informatics study program at Nurul Jadid University does not have a general concentration of knowledge, so that sometimes the selection of elective courses by students is not quite right. This study aims to classify the concentration of knowledge with a data mining approach which can then be used as a recommendation for selecting elective courses by students. In this study, we implement a machine learning algorithm to provide recommendations to students regarding what interests are more suitable to be taken based on the values ​​of prerequisite courses in previous semesters. Student data was obtained from the Head of the Center for Data and Information Systems (PDSI) at Nurul Jadid University with 70 student data from Nurul Jadid University batch 2018. The machine learning algorithm used is Neural Network with Python programming language, the tools used are Google Collab. At the beginning of data collection, then pre-processing is carried out to prepare the dataset in order to get good results, and model training is carried out. After training on the model, then further testing is carried out on the model to determine the performance of the model. The result of the accuracy value in the training model process is 0.83 or 83% and the accuracy of the test data is 0.79 or 79%.
OPTIMASI CHATBOT DALAM SISTEM PENGADUAN PELAYANAN PUBLIK BERBASIS ANDROID Tholib, Abu; Andi, Moh syaiful; Sukron, Moh; Shudiq, Wali Ja'far; Hairani, Hairani; Guterres, Juvinal Ximenes
Insand Comtech : Information Science and Computer Technology Journal Vol 10, No 1 (2025): Insand Comtech
Publisher : Universitas Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53712/jic.v10i1.2637

Abstract

This study presents the development of an Android-based public service complaint application integrated with chatbot technology to improve service responsiveness. The system aims to facilitate community members in submitting complaints and receiving immediate responses through an interactive interface. A user-friendly mobile application was developed using the Kotlin programming language, and chatbot functionality was implemented via API integration to respond to frequently asked questions. The implementation followed the Waterfall model, encompassing stages of analysis, design, implementation, testing, and maintenance. Results show that the application effectively streamlines the complaint process, increases efficiency in complaint management, and enhances communication between the public and local government. The chatbot proved to be reliable in delivering relevant and timely responses, significantly reducing the time needed for initial interactions. This integration demonstrates the potential of artificial intelligence to support e-government services in rural setting
OPTIMASI MODEL RESNET50 UNTUK KLASIFIKASI SAMPAH Sihabillah, Ahmad; Tholib, Abu; Basit, Illiyah Ibnul
Indexia Vol. 6 No. 2 (2024): INDEXIA : Informatics and Computational Intelligent Journal Volume 6 Nomor 2 No
Publisher : Universitas Muhammadiyah Gresik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30587/indexia.v6i2.9342

Abstract

Penelitian ini mengkaji pemanfaatan arsitektur ResNet50 dalam klasifikasi sampah organik dan anorganik untuk meningkatkan efisiensi pemilahan sampah secara otomatis. Dataset yang digunakan terdiri dari 12.565 gambar sampah organik dan 9.999 gambar sampah anorganik, mencakup berbagai variasi kondisi lingkungan, seperti pencahayaan, ukuran, dan bentuk. Tahapan penelitian meliputi preprocessing data, yang mencakup augmentasi gambar untuk menambah variasi, pembagian dataset menjadi data pelatihan dan validasi, serta penyesuaian bobot kelas untuk menangani ketidakseimbangan dataset. Model dilatih selama lima epoch dengan akurasi validasi tertinggi sebesar 80,15% pada epoch terakhir. Hasil evaluasi menggunakan metrik precision, recall, dan f1-score menunjukkan performa yang baik, dengan kategori sampah organik mencapai recall 91% dan f1-score 84%. Namun, kategori sampah anorganik memiliki precision sebesar 85% dengan recall yang lebih rendah, yaitu 67%. Analisis Confusion matrixmengungkapkan bahwa model mampu mengklasifikasikan sebagian besar sampel dengan benar, meskipun masih terdapat beberapa kesalahan pada kategori anorganik. Secara keseluruhan, penelitian ini membuktikan efektivitas ResNet50 dalam meningkatkan akurasi klasifikasi sampah, mendukung pengelolaan sampah yang berkelanjutan. Dengan optimisasi lebih lanjut, seperti penyesuaian hyperparameter atau augmentasi tambahan, model ini memiliki potensi untuk mencapai performa yang lebih tinggi dalam aplikasi praktis.
Peningkatan Usaha Kelompok Nelayan di UMKM Rusamin dengan IMS (Integrated Management System) Berbasis Web Codeigniter Tholib, Abu; Furqon, Ainul; Rahman, Taufiqur
TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora Vol 3, No 3 (2022)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/trilogi.v3i3.5115

Abstract

UMKM konvensional masih terkendala dalam hal promosi dan pemasaran produk, sehingga banyak pembeli yang berada diluar kota masih kurang mengetahui produk apa saja yang diproduksi sehingga omset penjualan tidak meningkat selain itu juga sering terjadi kekeliruan dan kesalahan dalam mencatat transaksi penjualan yang dilakukan yang dapat menyita waktu dalam pembuatan laporannya. Pengelolaan data penjualan juga belum optimal karena belum adanya distribusi data ke masing-masing bagian sehingga sering terjadi ketidakcocokan data antara bagian gudang. metode penelitian yang dilakukan menggunakan data penelitian kualitatif, yaitu penelitian yang dilakukan melalui Observasi dan wawancara di UMKM Rusamin. Hasil  penelitian ini menunjukkan bahwa penggunaan aplikasi  Framework Codeigniter dengan berbasis web di UMKM Rusamin dapat memudahkan pihak manajemen dalam monitoring penjualannya.
MANAJEMEN KLUSTERISASI PASAR: Penerapan Segmentasi Pelanggan Berbasis Metode Self-Organizing Map (SOM) di CV Karunia Probolinggo Tholib, Abu
TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora Vol 1, No 2 (2020): Manajemen Data Berbasis Teknologi Informasi
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (550.076 KB) | DOI: 10.33650/trilogi.v1i2.1897

Abstract

As one of the distributors engaged in the sale and distribution of cosmetics, CV Karunia is in charge of serving customers who have placed an order, so that each order delivery must be recorded properly. By grouping customers according to regions and orders, it will be easier for distributors to know which regions and whose customers have the largest number of orders. Therefore, CV Karunia must have a customer mapping strategy, for example by using the SOM (Self Organizing Maps) method which aims to facilitate marketing efforts and customer grouping according to customer desires and habits, in order to obtain maximum results. Through this SOM method, customer decision making and optimization of the customer service process can be done well.
Co-Authors Ahmad Baidowi Eko Fitra Firmanda Ahmad Halimi Ahmad Hudawi As ahmad taufiqul imam Alfan Maulan Andi, Moh syaiful Basit, Illiyah Ibnul Cahyuni Novia Deddy Junaedi Deniyanto Muchlizin Wahidillah Devita Alif Barmansyah Eka Wahyu Ramadhan Eliyanto, Andik Elfandiyono Erna Daniati Fadli Hidayat, M. Noer Fathorazi Nur Fajri Fauziah, Gustin Fitwatul Khoiriyah Furqon, Ainul Gulpi Qorik O tagalu .P Guterres, Juvinal Ximenes Hairani Hairani Halimi, Ahmad Ihsan, Gilang Hafidzul Inayatul Maula Inayatul Maula Itqan, Moh Syadidul Janhasmadja, Mengas Juvinal Ximenes Guterres Linda Uswatun Hasanah M. Erfan Rianto Marzuki, Muhammad Ismail Maulidiansyah Maulidiansyah Misbahul Munir Moh Ali Ishaq Moh Lailul Ilham Moh Syadidul Itqan Muafi Muafi Muafi Muh Nurul Imam Musfiroh Musfiroh Nanda Kurnia Agusmawati Qurrotu Aini Rahman, M Fadhilur Ratri Enggar Pawening Resty Wulanningrum Rian Hidayat Rizal Sulton Salman, Moh Setiyo Adi Nugroho Shudiq, Wali Ja'far Sihabillah, Ahmad Soleh, Paisal Sukron, Moh Supri yono Supriadi, Ahmad Syafiih, M Syaroni, Wahab Syaroni, Wahab Taufiqur Rahman, Taufiqur Tsabbit Albannani, Nur Wahyu Virda Virdausih Putri Wahab Syaroni Wahab Syaroni Wali Ja’far Shudiq Warda, Faridatul Wiwin Yuliana Ximenes Guterres, Juvinal Yaqin, Moh. Ainol Yayat Hidayat Yoga Yuniadi Yuliana, Wiwin Yuri Ariyanto Zain, Ahmad Naufal Waliyus Zainal Arifin Zainal Arifin