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All Journal Elkom: Jurnal Elektronika dan Komputer Syntax Literate: Jurnal Ilmiah Indonesia JMM (Jurnal Masyarakat Mandiri) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Tekno Kompak Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Ilmiah Intech : Information Technology Journal of UMUS Journal of Computer Networks, Architecture and High Performance Computing Jurnal Abadimas Gorontalo JUTECH : Journal Education and Technology Journal Automation Computer Information System (JACIS) Malcom: Indonesian Journal of Machine Learning and Computer Science QISTINA: Jurnal Multidisiplin Indonesia Jurnal Sintaks Logika (JSilog) Jurnal Indonesia Sosial Sains Innovative: Journal Of Social Science Research SmartComp Journal of Citizen Research and Development
Ramdhan, Nur Ariesanto
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Sistem Informasi Geografis Tingkat Kerusakan Ruas Jalan Berbasis Web Fauzi, Akhmad; Ramdhan, Nur Ariesanto; Khamid, Abdul
Journal of Citizen Research and Development Vol 1, No 2 (2024): November 2024
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/jcrd.v1i2.3330

Abstract

Jalan merupakan prasarana transportasi jalan dan jalur lalu lintas yang sangat penting. Salah satu fungsi jalan adalah berperan dalam mendorong pembangunan dan pemerataan pembangunan suatu daerah. Bagi pemerintah, jalan merupakan sarana transportasi yang mempengaruhi jalannya perekonomian dan pemerintahan. Saat ini Dinas Pekerjaan Umum Kabupaten Brebes masih kesulitan menyajikan informasi spasial dalam bentuk peta digital, masih kesulitan memelihara data status jalan, dan belum dipublikasikannya data status jalan menyebabkan kesulitan bagi masyarakat pada umumnya. mendapatkan. informasi mengenai kondisi jalan. Sistem informasi geografis menjadi solusi dalam menyajikan data spasial. Sistem informasi geografis merupakan suatu teknologi yang merupakan alat untuk menyimpan, mengolah, menganalisis dan menampilkan kondisi alam dengan menggunakan data. Sistem informasi berbasis web ini dirancang sesuai model UML (Unified Modeling Language) dengan bahasa pemrograman PHP (Hypertext Preprocessor) dan menggunakan MySQL sebagai databasenya. Aplikasi GIS yang digunakan adalah ArcView, MapServer dan Pmapper. Hasil penelitian menyimpulkan bahwa sistem informasi geografis Kabupaten Brebes tingkat kerusakan jalan diharapkan dapat bermanfaat bagi pemerintah khususnya Dinas Pekerjaan Umum Kabupaten Brebes dalam menyajikan informasi kerusakan jalan.
Implementasi Data Mining C4.5 dalam Mengukur Tingkat Kepuasan Mahasiswa terhadap Pelayanan Akademik Ujianto, Nurtulus; Ramdhan, Nur Ariesanto
Jurnal Teknik Informatika UMUS Vol 4 No 01 (2022): Mei
Publisher : Universitas Muhadi Setiabudi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46772/intech.v4i01.673

Abstract

Tujuan dari implementasi data Mining C4.5 ini untuk mengukur tingkat kepuasan mahasiswa terhadap pelayanan  akademik pada Program Studi Informatika Fakultas Teknik dan Ilmu Komputer di Universitas Pancasakti Tegal. Menggunakan lima aspek pada penilaian tingkat kepuasan, yaitu tangible,reability,assurance,responsiveness dan empathy. Kelima aspek tersebut belum terukur dengan pasti sehingga kesulitan dalam menentukan aspek yang kualitas yang harus di tingkatkan. Melalui metode algoritma C4.5, penulis mencoba mengukur dari lima aspek tersebut, sehingga terbentuk sebuah pohon keputusan. Setelah dilakukan perhitungan secara manual, maka dilakukan pula pembuktian menggunakan aplikasi, yaitu RapidMiner. Dari proses implementasi analisis, dapat diketahui bahwa aspek pelayanan pada staf akademik merupakan aspek yang paling dominan dalam menentukan tingkat kepuasan mahasiswa.
Penerapan Algoritma C4.5 untuk Prediksi Prioritas Relokasi Rumah Terdampak Bencana Tanah Gerak di Kabupaten Brebes Apriliani, Rosema Dina; Ramdhan, Nur Ariesanto; Bachri, Otong Saeful
Innovative: Journal Of Social Science Research Vol. 5 No. 4 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i4.20672

Abstract

Bencana tanah gerak di Kabupaten Brebes menyebabkan kerusakan parah pada hunian, sehingga diperlukan sistem pendukung keputusan untuk menentukan prioritas relokasi rumah secara cepat dan akurat. Penelitian ini mengembangkan aplikasi berbasis web menggunakan metode Waterfall dan algoritma Decision Tree C4.5. Data diperoleh dari DINPERWASKIM Brebes dengan atribut seperti jenis bencana, lokasi, status kepemilikan rumah, dan kondisi bangunan (atap, kolom/balok, struktur). Implementasi dilakukan menggunakan Flask dan PostgreSQL, sedangkan evaluasi mencakup pengujian fungsional (Black-Box) dan performa model. Hasil pengujian menunjukkan aplikasi berfungsi sesuai kebutuhan dan model C4.5 mencapai akurasi 98,89% setelah penyeimbangan kelas dengan SMOTE. Atribut kondisi kolom/balok dan kondisi atap menjadi faktor utama dalam penentuan prioritas. Sistem ini diharapkan mendukung pengambilan keputusan pemerintah daerah dalam program relokasi rumah terdampak bencana secara lebih tepat dan efisien.
Implementasi Metode Waterfall dalam Pengembangan Sistem Pengajuan Produk Hukum Daerah Berbasis Web dengan FIFO dan Tracking Valiant Krisnha, Arkana; Ramdhan, Nur Ariesanto; Premana, Agyztia
Elkom: Jurnal Elektronika dan Komputer Vol. 18 No. 1 (2025): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v18i1.2954

Abstract

Digital transformation in government bureaucracy has become a strategic step to improve efficiency, accountability, and transparency, including in the management of regional legal products. In the Regional Secretariat of Tegal Regency, the process of submitting legal products is still done manually, which causes inefficiencies, delays, and a lack of document traceability. To address these issues, a web-based legal product submission application has been developed with document tracking features. This research uses the Waterfall system development method, and implements a FIFO (First In First Out) queuing system in the submission process, along with Role-Based Access Control (RBAC) for managing user access rights. The goal of this system is to create a faster, more transparent, and digitally documented application process. The implementation results show that the application is able to systematically and integratively manage the flow of submissions, corrections, verification, and the ratification of legal products. Features such as a Login page with CAPTCHA, analysis dashboards, tracking, and monthly reports enhance the monitoring and security functions of the system. This application can be an effective solution in supporting the digitization of regional legal bureaucracy, as well as providing ease and efficiency for regional officials in preparing and submitting drafts of legal products digitally.
Transformasi Digital Aset Desa dengan Sistem Informasi Geografis untuk Pemetaan Rumah dan Tanah Warga Mariska, Novita Septi; Bachri, Otong Saiful; Ramdhan, Nur Ariesanto
Journal Automation Computer Information System Vol. 5 No. 2 (2025): November
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v5i2.128

Abstract

Perkembangan teknologi informasi mendorong pemanfaatan Sistem Informasi Geografis (SIG) sebagai solusi modern dalam pengelolaan aset desa yang selama ini masih dilakukan secara manual dan rawan kesalahan. Kondisi tersebut menimbulkan masalah keterlambatan verifikasi data, ketidaksesuaian arsip dengan kondisi lapangan, serta kurangnya transparansi informasi publik. Penelitian ini menggunakan pendekatan research and development dengan model pengembangan perangkat lunak Waterfall, yang mencakup analisis kebutuhan, perancangan, implementasi, dan pengujian fungsional. Populasi penelitian mencakup seluruh aset rumah dan tanah warga di Blok 014 Desa Panggangsari, dengan sampel dipilih secara purposive sampling berdasarkan kelengkapan data kepemilikan. Aplikasi SIG berbasis web yang dikembangkan mampu menampilkan dashboard ringkas, peta interaktif dengan marker koordinat, serta fitur pencarian berbasis nama pemilik. Proses digitalisasi arsip menghasilkan 96 entri valid yang terintegrasi ke dalam basis data spasial, dengan rata-rata luas tanah antar-RT bervariasi dari 133,6 m² hingga 205,9 m². Sistem terbukti mempercepat pencarian data, mempermudah verifikasi kepemilikan, serta meningkatkan transparansi dibandingkan metode manual. Adapun kontribusi utama penelitian ini adalah integrasi data spasial dan atribut kepemilikan dalam satu platform yang dapat dimanfaatkan perangkat desa untuk perencanaan pembangunan, distribusi bantuan, dan pengelolaan aset secara sistematis. Arah pengembangan ke depan meliputi pemutakhiran data melalui survei lapangan, penambahan fitur analisis spasial lanjutan, dan uji penerimaan pengguna guna meningkatkan kegunaan serta keberlanjutan sistem.
Implementasi Algoritma K-Means Clustering Terhadap Penjualan Produk Pada AAS Toserba Jatibarang Khaerunnisa, Rindiyani; Irawan, Bambang; Ramdhan, Nur Ariesanto; Shahib, Muhammad Umar
Jurnal Tekno Kompak Vol 18, No 2 (2024): AGUSTUS
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v18i2.4200

Abstract

Studi ini menyelidiki pola pembelian pelanggan di AAS Toserba Jatibarang mengalami kesulitan dalam menentukan jumlah stok barang dengan adanya penelitian dengan menggunakan algoritma Clustering K-Means. Data penjualan dimasukkan ke dalam tiga kelompok: "Tidak Laris", "Laris", dan "Paling Laris". Hasilnya menunjukkan perbedaan yang signifikan dalam perilaku pembelian antara kelompok pelanggan tersebut. Hasil ini dapat menambah literatur analisis pola pembelian konsumen menggunakan metode clustering dan memberikan wawasan yang berharga bagi manajemen toserba untuk meningkatkan strategi pemasaran dan penjualan. Dengan adanya penelitian ini penentua jumlah stok barang tidak lagi menggunakan manual.
Implementation of Markerless Augmented Reality For Human Anatomy Education Almesta, Adhika Yameraja; Bachri, Otong Saeful; Ramdhan, Nur Ariesanto
Jurnal Indonesia Sosial Sains Vol. 6 No. 9 (2025): Jurnal Indonesia Sosial Sains
Publisher : CV. Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jiss.v6i9.1900

Abstract

This research aims to develop and implement Augmented Reality (AR) technology as an interactive learning medium for introducing human body anatomy using a markerless method. The background of this research is the lack of interest and understanding among students regarding biology, particularly anatomy, which has been delivered through conventional methods such as textbooks or two-dimensional images. AR technology allows students to visualize three-dimensional models of human body parts directly in the real world, without the need for physical markers, thereby creating a more engaging, flexible, and accessible learning experience. The methods used in this research include planning stages, needs analysis, data collection through interviews and observations, application development using Unity and Vuforia SDK, and finally implementation and evaluation. The application developed was tested at SMAN 1 Bulakamba and resulted in an increase in students' understanding and interest in learning anatomy. The evaluation results show that the majority of students stated they understood the material very well after using this AR application, with high average scores on each questionnaire indicator. Thus, the application of markerless AR technology has proven effective as an interactive visual education tool and holds great potential for implementation in biology education as well as other subjects that require complex visualizations
Analisis Sentimen Ulasan Wisata Alun-Alun Brebes pada Google Maps Menggunakan Support Vector Machine Maulida, Azkiyatul; Irawan, Bambang; Ramdhan, Nur Ariesanto
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.8883

Abstract

The rapid development of information technology has encouraged the use of digital platforms as media for sharing opinions, including tourism reviews on Google Maps. Alun-Alun Brebes, as one of the most frequently visited public spaces, has generated thousands of reviews with diverse textual characteristics, making manual analysis inefficient and impractical. This study aims to analyze visitor sentiment toward Alun-Alun Brebes by applying a text mining approach using the Support Vector Machine algorithm. The dataset consists of 1,000 Google Maps reviews, including 327 reviews manually labeled as positive and negative sentiments and 673 unlabeled reviews. The research stages include data collection, text preprocessing, feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method, Support Vector Machine model training and testing, and automatic labeling of unlabeled data. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics based on the manually labeled data. The results show that the Support Vector Machine model with a linear kernel achieved an accuracy of 100% with an F1-score of 1.00, indicating excellent sentiment classification performance. Furthermore, word cloud visualization reveals that positive sentiment is dominated by aspects related to comfort and facilities, while negative sentiment is associated with cleanliness, crowd density, and environmental management. These findings provide data-driven insights into key aspects that should be maintained and improved in managing Alun-Alun Brebes as a public space.
Sentiment Analysis of Shopee User Reviews Using Recurrent Neural Network with LSTM for Real-Time Web-Based Prediction Qurani, Suci Ayu; Irawan, Bambang; Ramdhan, Nur Ariesanto
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Call for Paper for Machine Learning / Artificial Intelligence, Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7824

Abstract

Sentiment analysis has become an important approach for understanding user opinions on e-commerce platforms. Shopee user reviews provide valuable information that can be utilized to evaluate service quality and customer satisfaction. This study aims to analyze the sentiment of Shopee user reviews using a Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) architecture. The research method includes data collection, text preprocessing, model training, and performance evaluation. The experimental results show that the proposed RNN-LSTM model achieved an accuracy of 97%, indicating its effectiveness in classifying user sentiment. The developed model is further implemented in a web-based application to provide real-time sentiment prediction. The findings of this study demonstrate that the RNN-LSTM approach is suitable for sentiment analysis in e-commerce environments and can support decision-making based on user feedback.
Penerapan Algoritma Random Forest Prediksi Penyakit Paru-Paru Nur Ikhda, Sofi Nissa; Ramdhan, Nur Ariesanto; Premana, Agyztia
JATISI Vol 12 No 4 (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.v12i4.13854

Abstract

Lung disease is a serious respiratory disorder that requires early detection for proper treatment. The advancement of data mining technology enables the diagnostic process by analyzing patient behavioral data to predict the likelihood of developing lung disease. This study applies the Random Forest algorithm using data obtained from Kaggle, containing information such as age, gender, smoking habits, activities, and pre-existing conditions. The dataset was processed using the RapidMiner application through a data preprocessing phase, separating labeled and unlabeled data. A total of 998 records were used for model training and 29,002 for prediction. The model evaluation employed the Performance operator to measure prediction accuracy. The results show that the Random Forest algorithm achieved 94.33% accuracy with high precision and recall values, proving its effectiveness in handling large datasets and supporting early detection of lung disease.
Co-Authors Abdul Hamid Abdul Khamid, Abdul Akhmad Fauzi Almesta, Adhika Yameraja Apriliani, Rosema Dina Awaliyah, Selvi Bachri, Otong Saiful Bambang Irawan Bambang Irawan /E21207042 Faqih, Abdulah Ghufroni, Ghufroni Ibnu Salam, Ibnu Jiwangga, Alifah Tembang Khaerunnisa, Rindiyani Laelia Nurpratiwiningsih, Laelia Mariska, Novita Septi Maulida, Azkiyatul MUHAMMAD SYAIFULLOH Mumpuni, Atikah Nugroho Heri Pramono Nur Ikhda, Sofi Nissa Otong Saeful Bachri Pradipta, Rengga Hanif Habib Prasetya, Adetya Yudha Prasetyo Yuli Kurniawan Prasetyo, Tobiryanto Premana, Agyztia Qurani, Suci Ayu Raden Mohamad Herdian Bhakti Shahib, Muhammad Umar Suci Atiningsih, Suci Susilo, Bayu Syahrina, Fadhillah Alvie Syaogi, Moh. Triadiani, Nadia Sukma Ujianto, Nur Tulus Ujianto, Nurtulus Valiant Krisnha, Arkana Wahyuningsih, Puji