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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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OPTIMASI KEAMANAN DATA PENERIMAAN MAHASISWA MENGGUNAKAN AES-256, SHA-256, DAN BASE64 Ahmad Halimi; Abu Tholib; Moh. Ainol Yaqin
JUSTIFY : Jurnal Sistem Informasi Ibrahimy Vol. 3 No. 1 (2024): JUSTIFY : Jurnal Sistem Informasi Ibrahimy
Publisher : Fakultas Sains dan Teknologi, Universitas Ibrahimy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/justify.v3i1.5107

Abstract

In this era of extraordinary information technology, data security is a priority, especially the process of admitting new students (PMB) to tertiary institutions. This process involves collecting sensitive personal data from thousands of prospective students each year. To protect this data, this research applies 256-bit Advanced Encryption Standard (AES), Secure Hash Algorithm 256 (SHA-256), and Base64 encryption methods. AES-256-CBC is known to be effective in maintaining data security with a high level of security. SHA-256 enhances security further by generating a unique hash that verifies data integrity. Meanwhile, Base64 converts binary data into a more manageable text format. This research also includes testing encryption and verification speed using the Laravel framework. The application of this method is expected to increase trust and meet strict data security standards in the PMB information system, guarantee comprehensive data protection and improve system integrity.
KLASIFIKASI DATA MINING DI TINGKAT KEPUASAN MAHASISWA TERHADAP PELAYANAN SISTEM INFORMASI FAKULTAS TEKNIK UNIVERSITAS NURUL JADID Zain, Ahmad Naufal Waliyus; Muafi, Muafi; Tholib, Abu
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 7 No 2 (2024): Jurnal SKANIKA Juli 2024
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v7i2.3200

Abstract

This research aims to assess impacts of information system services on student satisfaction to prevent dissatisfaction with campus information services. Students become active members of the academic community at higher education, are the center point of this investigation. Because the measurement of student satisfaction level on information services at the Faculty of Engineering is still unknown. By measuring student satisfaction, the faculty can improve the quality of service information system. The importance of campus information system services cannot be overstated because it serves as the main center for information management in higher education. in higher education. By using the Naïve Bayes Algorithm for the method used in this research utilizes simplicity and ease of application. its application. Data was collected through a questionnaire technique filled out by students of faculty of Engineering. The questionnaire contains about the quality of information systems and information service quality. A total of 316 student datasets were collected from 3 study programs in the Faculty of Engineering namely informatics, electrical engineering, and information technology study programs. Testing using naïve Bayes algorithm accuracy value of 94%, precision of 92% recall of 95%, and f1-score of 93%. It is hoped that this research can play an important role in improving the existing information system services to increase effectiveness. information system services to increase effectiveness.
Implementasi GridSearch dalam Meningkatkan Kinerja Model Support Vector Regresion (SVR) utuk Prediksi Penjualan Produk (Studi kasus : Meubel Rohman Jaya) Ahmad Baidowi Eko Fitra Firmanda; Ahmad Hudawi AS; Abu Tholib; Juvinal Ximenes Guterres

Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Yudharta Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35891/explorit.v16i1.5042

Abstract

In the era of digitalization, product sales forecasting plays a crucial role for companies in estimating future demand. Meubel Rohman Jaya, a furniture business established since 2010, requires accurate predictions to optimize stock availability with the variety of products they produce. This research aims to forecast furniture product sales using the Support Vector Regression (SVR) algorithm with GridSearch optimization. Sales data of 11 furniture products over 30 months (January 2021 - June 2023) were processed through data collection and preprocessing. Modeling was performed using SVR without optimization and SVR with GridSearch optimization to obtain the best parameters. Predictions were generated and then evaluated using the Mean Absolute Percentage Error (MAPE) metric. The results showed that SVR without optimization achieved a MAPE of 40.39%, while SVR with GridSearch achieved a MAPE of 0.45%, indicating a significant increase in accuracy. GridSearch optimization has proven effective in improving prediction performance and is highly recommended for implementation in forecasting product sales at Meubel Rohman Jaya.
Pendampingan Program Start-Up Bisnis Menuju Sekolah Pencetak Wirausaha di Kabupaten Probolinggo Tholib, Abu; Pawening, Ratri Enggar; Junaedi, Deddy
Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Vol. 5 No. 1 (2024): November 2024
Publisher : Cipta Media Harmoni

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53624/kontribusi.v5i1.496

Abstract

Latar Belakang: Program pendampingan ini dilaksanakan untuk mengatasi keterbatasan pemahaman kewirausahaan digital di kalangan SMK di Kabupaten Probolinggo. Tujuan: Kegiatan ini bertujuan membangun ekosistem kewirausahaan melalui pengembangan keterampilan manajerial dan teknis kepala sekolah, guru, dan siswa SMK. Metode: Dalam kolaborasi dengan MKKS SMK Kabupaten Probolinggo, metode yang digunakan mencakup sosialisasi, pelatihan kewirausahaan, penerapan teknologi melalui aplikasi SPW berbasis web, dan pendampingan berkelanjutan. Hasil: Hasilnya menunjukkan peningkatan signifikan pada keterampilan manajerial dan teknis di 47 sekolah mitra, yang kini lebih siap membimbing siswa dalam mengembangkan bisnis. Kesimpulan: Aplikasi SPW juga terbukti memudahkan sekolah dalam pemantauan dan evaluasi kewirausahaan, meningkatkan efisiensi manajemen bisnis di tingkat sekolah.
Thesis Topic Modeling Study: Latent Dirichlet Allocation (LDA) and Machine Learning Approach Hairani, Hairani; Janhasmadja, Mengas; Tholib, Abu; Ximenes Guterres, Juvinal; Ariyanto, Yuri
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 3 No 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4375

Abstract

The thesis reports housed in the campus repository have yet to be analyzed to reveal valuable knowledge patterns. Analyzing trends in thesis research topics can facilitate the selection of research topics, aid in mapping research areas, and identify underexplored topics.Therefore, this research aims to model and classify thesis topics using Latent Dirichlet Allocation (LDA) and the Naïve Bayes and Support Vector Machine (SVM) methods. This study employs the LDA method for thesis topic modeling, while SVM and Naïve Bayes are used for classifying these topics. The research results show that LDA successfully modeled five of the most popular thesis topics, namely two related to computer networks, two on software engineering, and one on multimedia. For thesis topic classification, the SVM method demonstrated higher accuracy than Naïve Bayes, reaching 92.80% after the data was balanced using Synthetic Minority Oversampling Technique (SMOTE). The implication of this study is that the topic modeling approach using LDA is able to identify dominant thesis topics. In addition, the SVM classification results obtained better accuracy than Naïve Bayes in the thesis topic classification task.
Pengembangan Sistem Informasi MA Zainul Anwar Kraksaan Berbasis Web dengan Sistem Multi User Manejemen Tholib, Abu; Marzuki, Muhammad Ismail; Tsabbit Albannani, Nur Wahyu; Ihsan, Gilang Hafidzul; Salman, Moh
TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora Vol 5, No 4 (2024)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/trilogi.v5i4.9721

Abstract

The lack of effective information dissemination methods at MA Zainul Anwar Kraksaan has hindered the community and parents' understanding of school programs. This research aims to develop a web-based information system featuring multi-user management to address these challenges. Using the Research and Development (R&D) approach with the Waterfall model, the study involved stages of need analysis, system design, implementation, testing, and maintenance. Data were collected through observations and interviews, and the findings were used to design a user-friendly system tailored to the school’s needs. The results demonstrate that the developed system significantly improves communication efficiency and transparency, streamlines administrative processes, and provides a robust information portal. The study recommends adopting similar systems in other schools facing comparable challenges to enhance communication and operational efficiency.
Implementasi AI Chatbot Sebagai Support Assistant Website Universitas Nurul Jadid Menggunakan Algoritma Long Short-Term Memory (LSTM) M. Erfan Rianto; Maulidiansyah Maulidiansyah; Abu Tholib
Journal of Electrical Engineering and Computer (JEECOM) Vol 6, No 1 (2024)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v6i1.8556

Abstract

Perkembangan teknologi semakin pesat, menciptakan perubahan besar dalam berbagai aspek kehidupan termasuk pada sektor pendidikan. Universitas Nurul Jadid merupakan lembaga pendidikan yang perlu mengadaptasi teknologi terkini untuk efisiensi dan pelayanan untuk menjawab peningkatan volome pertanyaan dan informasi yang dibutuhkan masyarakat/orangtua sebelum mendaftarkan putra-putrinya kuliah di Universitas Nurul Jadid. Chatbot merupakan bagian dari Natural Languange Processing (NLP) berbasis Artificial Intelegent (AI) yang berfungsi melakukan percakapan dengan pengguna melalui teks atau ucapan yang memberikan layanan cepat dan akurat sepanjang waktu. Long Short-Term Memory (LSTM) yaitu algoritma deep learning untuk memprediksi serta klasifikasi data teks. Data penelitian terdiri dari tag, pattern dan response yang diperoleh secara manual dari referensi website Universitas Nurul Jadid kemudian di preprocessing guna membuat model. Bagian utama pada model chatbot ini yaitu lapisan embedding yang memberikan nilai vektor untuk setiap kata dalam data teks yang telah dimasukkan. Hasil training model menghasilkan akurasi sebesar 99.32% dan loss sebesar 12.57% Ini menadakan model sudah bagus dan tidak terjadi overfitting atau underfitting sehingga model layak untuk dilakukan pengujian dan deployment. Hasil ini mendukung penggunaan chatbot LSTM sebagai asisten virtual untuk membantu masyarakat/calon mahasiswa/mahasiswa mengakses informasi.
Analisis Sentimen Terhadap Ulasan Aplikasi Shopee di Google Play Store Menggunakan Metode TF-IDF dan Long Short-Term Memory) Musfiroh Musfiroh; Abu Tholib; Zainal Arifin
Journal of Electrical Engineering and Computer (JEECOM) Vol 6, No 2 (2024)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v6i2.8713

Abstract

Pengunjung Shopee semakin meningkat dari tahun 2022 hingga 2023. Karena peningkatan itu, semakin banyak pengguna yang berkomentar negatif atau positif. Maka, mengetahui sentimen pengguna pada aplikasi Shopee dapat mengetahui perilaku pelanggan dan meningkatkan penjualan. Penelitian ini menggunakan metode TF-IDF dan algoritma LSTM. Adapun tahapan penelitian seperti scrapping data yang menggunakan ulasan pengguna aplikasi Shopee di Google Play Store sebanyak 3565 data. Lalu data dikategorikan menjadi tiga kelas: positif, netral, dan negatif. Proses preprocessing meliputi Tokenization, Normalization, Stopword, dan Stemming. Selanjutnya dilakukan proses train data dan data test sebesar 8:2. Lalu melakukan vektorisasi dengan TF-IDF, melatih model dengan penggabungan TF-IDF dan LSTM (Long Short-Term Memory), serta menggunakan metrics untuk mengevaluasi model dan visualisasi menggunakan word cloud. menghasilkan akurasi sebesar 83% dengan nilai loss (kerugian) sebesar 0.1385. Model memiliki kemampuan cukup baik dalam memprediksi kelas negatif dan positif tetapi kurang efektif untuk kelas netral karena data yang kurang seimbang. 
Application of the Attention-Based LSTM Method for Rainfall Prediction in East Java Arifin, Zainal; Tholib, Abu; Hidayat, Rian
International Journal of Computer and Information System (IJCIS) Vol 5, No 4 (2024): IJCIS : Vol 5 - Issue 4 - 2024
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v5i4.224

Abstract

This research aims to measure the performance of the Attention-Based Long Short-Term Memory (LSTM) predictive model in rainfall prediction analysis in East Java, with a focus on including the application of the model in predicting complex time-series data. The main objective of this study is to create an efficient and accurate model and to emit the performance of the Attention-Based LSTM algorithm compared to conventional methods. The methodology used includes rainfall data collection, data preprocessing, Attention-Based LSTM model design, training models, and testing to assess accuracy. The results of the study indicate that the Attention-Based LSTM model is able to improve rainfall prediction compared to conventional methods, with the Root Mean Squared Error (RMSE) evaluation metrics with a value of 0.00807 and Mean Squared Error (MSE) with a value of 0.08987 which shows better results, so this model can be relied on for real-world applications.
PENDAMPINGAN DAN PELATIHAN 5 SOFT SKILL DASAR UNTUK PERSIAPAN DUNIA KERJA DI MA ZAINUL ANWAR KRAKSAAN Abu Tholib; Muh Nurul Imam; Eka Wahyu Ramadhan; Alfan Maulan; Moh Lailul Ilham; Moh Ali Ishaq; Misbahul Munir
SINAR: Sinergi Pengabdian dan Inovasi untuk Masyarakat Vol 1 No 01 (2024): Oktober
Publisher : CV. Laskar Karya

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

Abstract

Masalah utama yang dihadapi oleh MA Zainul Anwar Kraksaan adalah rendahnya keterampilan lunak (soft skills) yang dimiliki oleh siswa, terutama dalam bidang komunikasi, kerjasama tim, manajemen waktu, pemecahan masalah, dan kepemimpinan. Kurangnya pemahaman dan penerapan keterampilan ini dapat menghambat siswa dalam mempersiapkan diri memasuki dunia kerja. Oleh karena itu, pengabdian ini bertujuan untuk meningkatkan keterampilan lunak siswa melalui program pendampingan dan pelatihan yang terstruktur. Metode pengabdian yang digunakan meliputi identifikasi kebutuhan melalui survei awal, penyusunan modul pelatihan, pelaksanaan serangkaian workshop, sesi mentoring, dan simulasi dunia kerja. Program ini berhasil meningkatkan pemahaman dan penerapan soft skills pada siswa MA Zainul Anwar. Evaluasi menunjukkan adanya peningkatan signifikan dalam kemampuan siswa dalam mengaplikasikan keterampilan lunak, yang diharapkan dapat mendukung kesiapan mereka menghadapi tantangan dunia kerja. Rekomendasi dari pengabdian ini adalah perlunya integrasi pengembangan soft skills dalam kurikulum sekolah agar keberlanjutan program dapat terjaga.
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