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All Journal JOIN (Jurnal Online Informatika) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Tekno Insentif Jurnal Sistem Cerdas Jurnal Teknologi Terapan JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Teknik Industri Terintegrasi (JUTIN) Competitive Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi The Indonesian Journal of Computer Science
Habibi, Roni
Unknown Affiliation
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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KosConnect dengan Metode Google OAuth dan Payment Gateway Midtrans Azzahra, Fathya Fathimah; Rosa Sekamayang, Balqis; Habibi, Roni
Jurnal PROCESSOR Vol 20 No 1 (2025): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2025.20.1.2185

Abstract

KosConnect is an innovative application that facilitates boarding house search, booking, and payment by integrating modern technologies to enhance user experience. As the demand for boarding houses increases and access to reliable information remains a challenge, KosConnect leverages Google OAuth for Single Sign-On (SSO) authentication, providing a more efficient and secure login process, while Midtrans serves as a payment gateway to enable fast and secure digital transactions. Developed using the Go programming language and MongoDB database, the application ensures optimal performance in data management while also offering personalization features to help users find accommodations that match their preferences. This project aims to streamline the login, registration, and payment processes by providing an integrated service that covers all stages from searching for boarding houses to completing transactions. The development results show that KosConnect effectively addresses the limitations of previous boarding house information systems, such as authentication that relies solely on email and password without SSO, bank transfers requiring manual proof uploads, and the lack of personalization features.
DETEKSI EMOSI PADA TEKS BERBAHASA INDONESIA MENGGUNAKAN PENDEKATAN ENSEMBLE Pane, Syafrial Fachri; Abdullah, Faisal; Habibi, Roni
Jurnal Teknologi Terapan Vol 10, No 2 (2024): Jurnal Teknologi Terapan
Publisher : P3M Politeknik Negeri Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31884/jtt.v10i2.551

Abstract

Emotion in written text is often difficult to recognize due to the absence of visual cues such as facial expressions or vocal intonation, which typically aid in understanding a person's feelings. This research aims to address this challenge by developing an emotion detection model for Indonesian text. The approach used is Ensemble Learning, combining three Machine Learning models: SVM, KNN, and XGBoost, to optimize emotion detection results. The main contribution of this research is the implementation of the Ensemble method for detecting emotions in Indonesian text, with performance evaluated using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. The evaluation results show that the Ensemble model outperforms previous models, achieving an accuracy, precision, recall, and F1 score of 87.14%, and a ROC AUC score of 97.90%. To further enhance performance, this study utilizes GridSearchCV for hyperparameter tuning of the SVM and XGBoost models and employs the Automated Machine Learning (AutoML) tool TPOT to generate the KNN model.
Penerapan Algoritma K-Nearest Neighbors untuk Deteksi Serangan Network Flood Berbasis Supervised Learning habibi, roni; Widana, Naufal Dekha
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 4 No. 2 (2025)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v4.i2.38599

Abstract

Deteksi anomali akibat serangan flood merupakan tantangan utama dalam pengelolaan keamanan jaringan modern. Penelitian ini mengusulkan penerapan algoritma K-Nearest Neighbors (KNN) dalam kerangka supervised learning untuk membangun model Network Flood Detection (NFD) yang dievaluasi menggunakan metrik performa yang lebih komprehensif, yaitu akurasi, presisi, dan recall. Model dikembangkan berdasarkan fitur jaringan seperti bandwidth masuk, bandwidth keluar, ping, serta distribusi trafik flood dan normal. Data diperoleh dari laporan jaringan instansi secara real-time dan historis, yang kemudian diproses melalui tahapan normalisasi, pengurangan fitur, dan penghapusan noise. Hasil evaluasi menunjukkan bahwa model mampu mencapai akurasi hingga 92,42% dengan skor F1 yang seimbang antar kelas. Selain itu, kurva ROC dengan AUC sebesar 0,99 menunjukkan bahwa model memiliki kemampuan diskriminasi yang tinggi dalam membedakan trafik flood dan normal. Temuan ini menunjukkan bahwa KNN, meskipun sederhana, dapat digunakan secara efektif dalam sistem deteksi serangan flood jika didukung oleh data yang representatif dan proses evaluasi yang tepat.
Sentiment Analysis on Social Distancing and Physical Distancing on Twitter Social Media using Recurrent Neural Network (RNN) Algorithm Nugraha, Fikri Aldi; Harani, Nisa Hanum; Habibi, Roni; Fatonah, Rd. Nuraini Siti
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.632

Abstract

The government is seeking preventive steps to reduce the risk of the spread of Covid-19, one of which is social restrictions that have become popular with social distancing and physical distancing. One way to assess whether the steps taken by the government regarding social and physical distancing are accepted or not by the community is by conducting sentiment analysis. The process of sentiment analysis is carried out using a variant of the Recurrent Neural Network (RNN), namely Long Short-Term Memory (LSTM). In this study, the results obtained from the sentiment analysis, where the public response to social distancing and physical distancing has more positive sentiments than negative sentiments. To measure the accuracy level of sentiment analysis using the Recurrent Neural Network (RNN) algorithm and evaluation of the modeling is done using confusion matrix where the results obtained for the training dataset are 89% accuracy, 89% recall, 89% precision, and 89% F1 Score. Meanwhile, for the test dataset, an accuracy of 80% was obtained, a recall of 79%, a precision of 81%, and an F1 score of 80%.
Penentuan rute terpendek antara dua titik di gudang menggunakan Dijkstra’s Algorithm dan Microsoft Excel Sanggala, Ekra; Pane, Syafrial Fachri; Habibi, Roni
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 8 No. 1 (2025): January
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v8i1.39060

Abstract

Sebuah gudang merupakan suatu faktor penting dalam logistik dan mempunyai peran vital dalam mengontrol dan mengurangi biaya logistik. Secara umum operasional pada gudang terdiri dari lima fungsi dasar, yaitu: receiving, sorting, storing, order picking dan delivering. Kecepatan pada order picking merupakan faktor penting untuk kepuasan pelanggan. Maka mempersingkat waktu order picking merupakan hal yang penting. Order Picking yang paling sederhana adalah saat produk yang dibutuhkan pelanggan hanya terletak pada satu rak saja, sehingga picker hanya perlu bergera dari titik awal menuju ke titik rak dimana produk berada. Permasalahan penentuan rute terpendek antara dua titik dapat didefinisikan sebagai Shortest Path Problem. Dijkstra’s Algorithm merupakan algoritma yang paling populer dalam menyelesaikan Shortest Path Problem. Untuk menyelesaikan Shortest Path Problem dengan Dijkstra’s Algorithm diperlukan sebuah tool yang dapat membantu menyelesaikan perhitungannya. Microsoft Excel merupakan salah satu tool yang sangat populer dan mudah digunakan untuk menyelesaikan berbagai perhitungan. Dengan mengkombinasikan berbagai formula yang terdapat pada Microsoft Excel terbukti bahwa perhitungan Dijkstra’s Algorithm untuk menyelesaikan Shortest Path Problem dapat dilakukan dengan baik.
Co-Authors Andarsyah, Roni Annisah, Wulan Nur Atmojo, Ariyo Azzahra, Fathya Fathimah Darfial Guslan Dwiyanti, Zian Asti Ekra Sanggala Fadilla Musdalifa, Deriska Faisal Abdullah Fatonah, Rd. Nuraini Siti Febrisa Sidabutar, Audry Harani, Nisa Hanum Hayati, Kurnia Maulana, Jaka Nugraha, Fikri Aldi Rahayu, Woro Isti Rahmawan, Fadhel Rosa Sekamayang, Balqis Salsabila, Helmi Setyawan, M. Yusril Helmi Syafrial Fachri Pane, Syafrial Fachri Widana, Naufal Dekha