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All Journal Jurnal Media Infotama Techno.Com: Jurnal Teknologi Informasi JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Informatika dan Teknik Elektro Terapan Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIV : International Journal on Informatics Visualization Indonesian Journal of Artificial Intelligence and Data Mining JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat To Maega: Jurnal Pengabdian Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi bit-Tech Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jurnal Abdi Insani Journal of Advanced in Information and Industrial Technology (JAIIT) Nusantara Science and Technology Proceedings Kalam Cendekia: Jurnal Ilmiah Kependidikan Journal of Information System and Technology (JOINT) TIERS Information Technology Journal International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Iqtisaduna COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Informatika Dan Tekonologi Komputer (JITEK) Jurnal Hasi Penelitian Dan Pengkajian Ilmiah Eksakta - JPPIE Jurnal Informatika Teknologi dan Sains (Jinteks) Concept: Journal of Social Humanities and Education JIKTI : Jurnal Ilmiah Komputer Terapan dan Informasi Mujtama': Jurnal Pengabdian Masyarakat Journal of Artificial Intelligence and Digital Business TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi ILTEK : Jurnal Teknologi Jurnal Elektronika dan Teknik Informatika Terapan Horizon: Indonesian Journal of Multidisciplinary Modem : Jurnal Informatika dan Sains Teknologi Joong-Ki Jurnal Informatika Dan Tekonologi Komputer Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis Mujtama’ Jurnal Pengabdian Masyarakat Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Muhammad Muharrom Al Haromainy
Universitas Pembangunan Nasional Veteran Jawa Timur
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PENGARUH RFE TERHADAP LOGISTIC REGRESSION DAN SUPPORT VECTOR MACHINE PADA ANALISIS SENTIMEN HOTEL SHANGRI-LA SURABAYA Maulana Herza, Fakhri; Rahmat, Basuki; Muharrom Al Haromainy, Muhammad
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 6 (2024): JATI Vol. 8 No. 6
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i6.11272

Abstract

Analisis sentimen merupakan alat penting dalam industri pariwisata untuk memahami respon dan pengalaman tamu terhadap layanan hotel, di mana ulasan tamu sebelumnya berperan krusial dalam membentuk persepsi calon tamu terhadap kualitas fasilitas dan daya tarik hotel. Tantangan utama dalam analisis sentimen adalah memilih fitur yang paling relevan untuk meningkatkan kinerja model prediksi, karena tidak semua kata atau fitur dalam ulasan memiliki kontribusi signifikan dalam membedakan sentimen positif dan negatif. Dalam konteks ini, metode Recursive Feature Elimination (RFE) memiliki potensi untuk mengoptimalkan pemilihan fitur dengan mengeliminasi fitur yang kurang informatif, sehingga dapat meningkatkan akurasi model Logistic Regression dan Support Vector Machine (SVM). Penelitian ini fokus pada pengaruh penerapan RFE terhadap kinerja kedua model tersebut dalam analisis sentimen ulasan tamu di Hotel Shangri-La Surabaya, dengan data sebanyak 3719 ulasan. Hasil pengujian menunjukkan bahwa pada model Logistic Regression yang menggunakan RFE, terdapat peningkatan performa yang signifikan dalam presisi, sensitivitas, F1 Score, dan akurasi, dengan rata-rata peningkatan sebesar 9%. Pada model SVM, peningkatan performa bahkan lebih signifikan dengan rata-rata peningkatan sebesar 14%, yang menunjukkan bahwa penerapan RFE secara efektif meningkatkan kualitas prediksi kedua model dalam konteks analisis sentimen ulasan hotel.
Comparison of Recurrent Neural Network and Naive Bayes Algorithms in Identifying Stunting in Toddlers Sujayanti, Forentina Kerti Pratiwi; Via, Yisti Vita; Haromainy, Muhammad Muharrom Al
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.33946

Abstract

Stunting in toddlers is a health issue that affects their quality of life. This study aims to predict stunting status using three classification methods: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gaussian Naive Bayes. The dataset from Kaggle was split into 70% for training and 30% for testing to ensure optimal model evaluation. The RNN model was built with three hidden layers of 64 units each, while the LSTM model had four hidden layers with the same number of units. Both models utilized hidden states to capture temporal patterns and employed the tanh activation function to detect complex data patterns. The ADAM optimizer with a learning rate of 0.001 was applied to accelerate convergence. In contrast, the Gaussian Naive Bayes model used a simple probabilistic approach without temporal patterns, making it suitable for simpler datasets. Evaluation using accuracy and RMSE showed that LSTM achieved the highest accuracy (91%), followed by RNN (90%), though both exhibited signs of overfitting. Gaussian Naive Bayes attained 72% accuracy with stable performance. While LSTM and RNN effectively capture complex temporal patterns, they are prone to overfitting, whereas Gaussian Naive Bayes is suitable for initial implementation or simpler datasets, supporting early intervention for stunted toddlers.
Perancangan Aplikasi Mobile Pembelajaran Bahasa Berbasis Podcast dan Penerapan Hybrid Recommender System Anugerah, Rico Putra; Nugroho, Budi; Al Haromainy, Muhammad Muharrom
CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Vol 11 No 1 (2025): CICES
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cices.v11i1.3509

Abstract

This research develops a mobile application based on podcasts to enhance language skills, particularly in listening. Listening is considered a priority because vocabulary comprehension is necessary before mastering speaking, reading, and writing. Podcasts are chosen as the learning medium due to their ability to present audio on specific topics, aiding users in more effectively understanding the language. The application is also equipped with a recommendation system that utilizes the Hybrid Recommender System method, tailoring content based on user behavior and content similarity. The research results show that this application can optimize language learning, with a particular focus on language learning through podcasts, and helps users improve their language skills with a precision value of 85,6%, recall 85,9%, F1-Measure 88% and 80,2 for SUS testing.
Rancang Bangun Sistem Evaluasi Kepuasan Akademis dan Analisis Topik pada Komentar Menggunakan Latent Dirichlet Allocation Volem Alvaro Azira Azira; Afina Lina Nurlaili; Muhammad Muharrom Al Haromainy
COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat Vol. 4 No. 12 (2025): COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/comserva.v4i12.3113

Abstract

Sistem evaluasi akademik sering bergantung pada alat pihak ketiga seperti Google Forms, yang kurang mampu menganalisis data teks secara mendalam. Gugus Kendali Mutu Fakultas (GKMF) Fakultas Ilmu Komputer UPN Veteran Jawa Timur membutuhkan sistem khusus untuk menganalisis umpan balik mahasiswa secara efisien. Penelitian ini bertujuan untuk (1) merancang sistem evaluasi kepuasan akademik berbasis web menggunakan MERN Stack, (2) menerapkan Latent Dirichlet Allocation (LDA) untuk analisis topik komentar mahasiswa, dan (3) mengintegrasikan Firebase Cloud Messaging (FCM) guna meningkatkan partisipasi melalui notifikasi otomatis. Sistem dikembangkan dengan MERN Stack, LDA untuk pemodelan topik (JavaScript/Node.js), dan FCM untuk notifikasi. Pengujian menggunakan blackbox testing dan USE Questionnaire. Sistem memperoleh skor kepuasan pengguna tinggi (kegunaan: 85,33%, kemudahan penggunaan: 86,67%). LDA menghasilkan 10 topik, dengan koherensi tertinggi (0,495) pada topik "peningkatan kualitas pengajaran." Penelitian ini menyediakan solusi terukur untuk analisis umpan balik akademik dan merekomendasikan pengembangan seperti analisis sentimen dan analitik real-time.
Implementasi Website Monitoring Pembayaran Siswa dengan Metode Prototyping dan Regresi Logistik Biner Siregar, Talitha Aurora Nadenggan; Nurlaili, Afina Lina; M. Muharrom Al Haromainy
Journal of Information System and Technology (JOINT) Vol. 6 No. 1 (2025): Journal of Information System and Technology (JOINT)
Publisher : Program Sarjana Sistem Informasi, Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/joint.v6i1.10358

Abstract

Pengelolaan pembayaran siswa yang tidak terstruktur seringkali menimbulkan masalah seperti kehilangan data dan kesulitan pemantauan. Penelitian ini bertujuan untuk mengembangkan aplikasi monitoring pembayaran siswa berbasis web di SMK Batik Sakti 2 Kebumen dan mengimplementasikan fitur prediksi keterlambatan pembayaran. Aplikasi ini dirancang menggunakan metode Prototyping dan dibangun dengan framework Laravel serta database MySQL. Fitur prediksi keterlambatan dikembangkan menggunakan algoritma Regresi Logistik Biner, yang dilatih dengan delapan variabel independen. Hasil pengujian menunjukkan bahwa model prediksi mencapai akurasi 93.75% pada data pelatihan dan 90% pada data validasi, serta aplikasi ini efektif membantu pengelolaan data pembayaran siswa.
OPTIMASI ALGORITMA K-NEAREST NEIGHBOR DENGAN ALGORITMA GENETIKA PADA DETEKSI PENYAKIT DIABETES MELLITUS Darmawan, Marcellinus Aditya Vitro; Haromainy, M. Muharrom Al; Junaidi, Achmad
JATISI Vol 12 No 2 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i2.11353

Abstract

This study discusses the optimization of the K-Nearest Neighbor (KNN) algorithm using Genetic Algorithm (GA) in detecting diabetes mellitus. The research includes stages of collecting datasets on diabetes mellitus symptoms, data preprocessing through normalization and dataset alignment, model implementation, and testing with various scenarios to achieve the highest accuracy. The data used consists of the Pima Indians Diabetes Database as dataset 1 and the Early Stage Diabetes Risk Prediction Dataset as dataset 2. The evaluation is conducted by comparing the accuracy results between KNN without optimization and KNN optimized using Genetic Algorithm. The study's results indicate that optimization is performed by finding the optimal combination of the k-value and the features used in classification. The Genetic Algorithm produces individuals with the best fitness based on the combination of k-values and features that yield the highest accuracy. Testing was conducted on two datasets with two different fold values. The best accuracy was obtained in the 10-fold test, where the accuracy for dataset 1 increased from 74.2% to 79.1% after optimization. Meanwhile, for dataset 2, the accuracy improved from 97.5% to 98.2% after optimization. There was an increase in accuracy for dataset 1, whereas for dataset 2, the improvement was not significant. The conclusion of this study is that optimizing the KNN algorithm using Genetic Algorithm has proven to enhance the accuracy of diabetes mellitus detection, especially in numerical datasets with more complex features.
IMPLEMENTASI METODE EXTREME PROGRAMMING PADA PEMBUATAN SISTEM INFORMASI PKL DAN PENGUJIAN WHITE BOX SERTA COMPUTER SYSTEM USABILITY QUESTIONNAIRE (CSUQ) Al Fatih, Abdullah; Muharrom Al Haromainy, Muhammad; Lina Nurlaili, Afina
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13514

Abstract

Perkembangan teknologi informasi telah mendorong peningkatan efisiensi di berbagai sektor, termasuk dalam pengelolaan Praktik Kerja Lapangan (PKL) di perguruan tinggi. Namun, pengelolaan data PKL yang melibatkan banyak pihak seperti mahasiswa, dosen, dan admin masih menghadapi kendala dalam hal efisiensi dan integrasi sistem. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi PKL yang dapat mengotomatisasi dan menyederhanakan proses administratif PKL. Metode yang digunakan dalam penelitian ini adalah Extreme Programming (XP) untuk pengembangan perangkat lunak dan White Box Testing untuk pengujian sistem. Selain itu, evaluasi kegunaan sistem dilakukan menggunakan Computer System Usability Questionnaire (CSUQ). Hasil penelitian menunjukkan bahwa sistem informasi PKL yang dikembangkan dapat mengatasi permasalahan operasional dan meningkatkan produktivitas kegiatan PKL dengan baik. Berdasarkan hasil survei CSUQ, skor rata-rata kepuasan pengguna adalah 6,19, yang menunjukkan tingkat kepuasan yang tinggi. Namun, terdapat area yang perlu perbaikan, terutama dalam aspek antarmuka pengguna. Sistem ini diharapkan dapat memberikan kontribusi dalam operasional kegiatan PKL di lingkungan prodi informatika Universitas Pembangunan Nasional Veteran Jawa Timur.
Application of IoT-based Intelligent Control Devices Empowered with Fuzzy Inference System in the Garment Industry Rizki, Agung Mustika; Ashari, Faisal; Yuliastuti, Gusti Eka; Haromainy, Muhammad Muharrom Al; Aditiawan, Firza Prima; Amnur, Hidra
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3344

Abstract

The garment industry in Indonesia has experienced significant development in recent years. A critical aspect of this development is the increasing role of Micro, Small, and Medium Enterprises (MSMEs). Swari Garment Industries (SGI) is an example of an MSME that focuses on the garment sector. In practice, various problems and negligence can affect the course of the production process. One potential issue is using the machine inappropriately or excessively, which can lead to a short electrical circuit. Short electrical circuits are one of the problems that must be faced because they can cause various severe impacts, including equipment damage and even fire. Based on this risk analysis, a possible solution to be applied to SGI, one of the MSMEs in the garment sector, is the implementation of an intelligent control device. The implementation of intelligent control tools based on the Internet of Things (IoT) can enhance the efficiency of the production process and mitigate significant risks to workers and the environment. The Fuzzy Inference System, in which the equity, temperature, and humidity are the input values of the Intelligent Control Device. A hardware device for temperature and humidity control, accessible through an Android phone application, was implemented in SGI. Experiments have verified that we can achieve excellent results. The average percentage of temperature measurement error was 0.2% and for humidity, 0.26%. The average percentage of measurement error from the comparison between the system and MATLAB is 0.49%.
PENGEMBANGAN SISTEM REKOMENDASI UNTUK SIMULASI RAKIT KOMPUTER MENGGUNAKAN ALGORITMA GENETIKA BERBASIS WEBSITE Maulana, Vieri Arief; Haromainy, Muhammad Muharrom Al; Nurlaili, Afina Lina
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 7 No 2 (2025): September 2025
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v7i2.491

Abstract

This research develops a web-based recommendation system for computer assembly simulations using genetic algorithms. The system is designed to assist users in selecting optimal computer components based on their available budget and desired performance. Component data were collected from e-commerce platforms and online sources, then preprocessed using Min-Max normalization to ensure balanced data scaling. The system was developed using Laravel for the frontend interface and Flask API for computational processing of the genetic algorithm. System evaluation was conducted using the System Usability Scale (SUS) method involving 21 respondents, resulting in an average score of 86.67, which falls into the "Excellent" category and Grade B on the usability scale. Additionally, performance comparisons with prebuilt systems from online stores show that the recommendation system produced assemblies with lower costs and higher performance. The implementation of selection, crossover, and mutation in the genetic algorithm effectively evaluates component combinations to achieve optimal configurations. This research contributes to the development of intelligent optimization-based systems that simplify the computer assembly process, particularly for novice users with limited technical knowledge and constrained budgets.
Pengembangan Bot Discord Sebagai Pemutar dan Rekomendasi Musik Menggunakan Metode K-Means Satrio, Deva Dwi; Akbar, Fawwaz Ali; Al Haromainy, Muhammad Muharrom
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 13, No 1: April 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v13i1.1681

Abstract

In the digital age, video gaming and music streaming have experienced rapid growth. Many gamers face challenges in time efficiency while playing, particularly when listening to music and communicating with teammates during video games. The Discord platform, providing voice channel spaces for gamers to communicate and command Discord bots, offers a potential solution to time efficiency issues. This paper aims to create an efficient Discord bot capable of searching, streaming, and providing recommendations to users to streamline their communication and gaming experiences. By integrating Spotify and YouTube APIs for attribute retrieval and music streaming, the K-Means method can be applied within the Spotify API for music recommendations. Implementing a Discord bot that can stream and recommend music aims to enhance the focused and enjoyable gaming experience for players.Keyword: K-Means Clustering; Bot Discord; Spotify Music Recommendation System; Application  AbstrakDalam era serba digital, permainan video dan streaming musik berkembang sangat pesat. Banyak gamer menghadapi tantangan efisiensi waktu saat bermain, terutama dalam hal mendengarkan musik dan berkomunikasi dengan rekan tim dalam permainan video. Dengan adanya platform Discord yang menyediakan ruang kanal suara untuk gamer berkomunikasi serta memerintah bot Discord maka efisiensi waktu akan sangat memungkinkan. Penulisan paper ini bertujuan menciptakan bot Discord yang secara efisien dapat melakukan pencarian, streaming, dan memberikan rekomendasi kepada pengguna agar dapat meringkas waktu ketika berkomunikasi dan bermain permainan video. Dengan mengintegrasikan API Spotify dan API Youtube untuk melakukan pengambilan atribut dan menyiarkan musik, metode K-Means dapat diterapkan dalam API Spotify untuk melakukan rekomendasi musik. Dengan mengimplementasikan bot Discord yang bisa menyiarkan serta merekomendasikan musik, maka akan tercipta pengalaman bermain yang lebih terfokus serta menyenangkan bagi para gamer. 
Co-Authors Abdillah, Ikhwan Abdul Rezha Efrat Najaf Achmad Andrian Maulana Achmad Junaidi Achmad Rozy Priambodo Agung Mustika Rizki, Agung Mustika Agus Wibowo Agus Zainal Arifin Ahmad Saikhu Akbar, Fawwaz Ali Al Danny Rian Wibisono Al Fatih, Abdullah Alya Izzah Zalfa Rihadah Ramadhani Nirwana Putri Ananda Ayu Puspitaningrum Andreas Nugroho Sihananto Angga Lisdiyanto Anggraini Puspita Sari Anggraini Puspita Sari Anita Puspitasari Annisa Dwi Puspitarini Anugerah, Rico Putra Arraya Akhsa Putra Priyadizah ASHARI, FAISAL Avi Sunani Aviolla Terza Damaliana Azira, Volem Alvaro Azira Basuki Rahmat Masdi Siduppa Bima Arya Kurniawan Budi Nugroho Chairil, Augustin Mustika Chastine Fatichah Christianty, Theressa Marry Clara Diva Paramitha Darmawan, Marcellinus Aditya Vitro Dhimas Wahyu Prayogi Dian Maharani Dinda Friska Oktaviana Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Edi Sugiyanto Eva Yulia Puspaningrum Fania Imelda Safitri Faris Syaifulloh Farkhan Fauzi, Zaky Ahmad Ferdi Firdaus Ega Pratama Ferry Trilaksana Putra Fetty Tri Anggraeny Firza Prima Aditiawan Fitrani, Laqma Dica Ganal Arief Rahmawan Gusti Eka Yuliastuti Hajjar, Debrina Octrisya Hardiansyah, In Naka Malik Henni Endah Wahanani Hidra Amnur I Wayan Alston Argodi I Wayan Alston Argodi Istian Kriya Almanfakulti Jeziano Rizkita Boyas Kartini Kartini Kevin Iansyah Kusuma Wardani, Amalia Dwi Lailatul Musyaffaah Lina Nurlaili, Afina Lintang Putri Permatasari Lusi Kurnia Lusian Nandang Arjamulia M. Sa’aduddin Abdillah Yusuf Mandyartha, Eka Prakarsa Maulana Herza, Fakhri Maulana, Hendra Maulana, Vieri Arief Moh. Angga Ardiyansyah Mohammad Habim Hazidan Rifqi Mohammad Setyo Wardono Muhamad Fihris Aldama Muhammad Albert Nur Agathon Muhammad Baihaqi Arrisalah Muhammad Daffa Arifin Muhammad Helmi Satria Fedianto Muhammad Izdihar Alwin Muhammad Rifaldi Syaril Mashafy Muzdalifah, Nayani Alya Aquila Nia Dwi Puspitasari Nugroho, Budi Nur Nafisatul Fitriyah Nurlaili, Afina Lina Nurlaili, Afina Lina Permatasari, Reisa Pratama Wirya Atmaja Prinafsika Putra, Chrystia Aji Putra, Gredy Christian Hendrawan Raden Kokoh Haryo Putro Rafie Ishaq Maulana Rafif Ilafi Wahyu Gunawan Ramadhani, Muhammad Nabil Retno Mumpuni Reza, Reno Alfa Riza Satria Putra Rizka Fadhillah, Irnanda Ryan Purnomo Samodera, Bayu Sari, Rizky Buana Satrio, Deva Dwi Setyawan, Dimas Ari Shalehuddin Albawani, Raden Siregar, Talitha Aurora Nadenggan Sujayanti, Forentina Kerti Pratiwi Suprapti Taufiqqurrahman, Husain Tompo Panjaitan Tri Septianto Trimono, Trimono Triyana, Dimas Volem Alvaro Azira Azira Wahyu Eko Pujianto Wahyu Fahrul Ridho Wahyu Syaifullah JS Waluya, Onny Kartika Waskito, Achmad Derajat Winarti ., Winarti Yisti Vita Via