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All Journal Elkom: Jurnal Elektronika dan Komputer Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Infotek : Jurnal Informatika dan Teknologi JUTECH : Journal Education and Technology Journal of Education Technology Information Social Sciences and Health
Nur Ariesanto Ramdhan
Universitas Muhadi Setiabudi
Computer Science & IT Control & Systems Engineering Education Engineering Languange, Linguistic, Communication & Media Neuroscience Nursing Public Health Other

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 1 
Mapping of K-Means Clustering Crime Prone Areas in Brebes Regency Otong Saeful Bachri; Nur Ariesanto Ramdhan; Teuku Rizal Adi Pangestu
Journal of Education Technology Information Social Sciences and Health Vol 3, No 2 (2024): September 2024
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/jetish.v3i2.3347

Abstract

This research aims to map crime-prone areas in the Brebes Police area using the K-Means Clustering method. The crime data used in this research was collected from weekly police reports in the Brebes Police area during 2023. The K-Means Clustering method was chosen because of its ability to group data based on similar characteristics, making it easier to identify crime patterns in various areas. The data was analyzed using rapidminer software to perform clustering, and the results were visualized in the form of a web-based interactive map developed using Visual Studio. The clustering results show that the Brebes area can be categorized into three levels of vulnerability: moderately vulnerable, vulnerable and very vulnerable. This mapping provides a clear picture of the distribution of crime rates in various regions, helping the police in designing more effective and efficient handling strategies. The system developed also provides features for accessing detailed data regarding the type and frequency of criminal acts in each area, which can be used by the Brebes Police and the general public. The implementation of this system is expected to increase the efficiency of crime data management, facilitate access to information, and support more targeted preventive and enforcement efforts. In addition, with information that is more structured and easily accessible, people can be more aware of potential threats in their surrounding environment. This research shows that the use of technology in managing crime data can make a significant contribution to increasing security and order in society. The web application system for mapping crime-prone areas using K-Means Clustering in the Brebes Police area was successfully developed and implemented, providing accurate and useful information for efforts to prevent and handle crime.
Analisis Spasial Jalur Pendakian Gunung Lawu via Cemoro Sewu Berbasis SIG Andin Ayu Oksilia Ramadhani; Nur Ariesanto Ramdhan; Bambang Irawan
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 2 (2026): April 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i2.3583

Abstract

This study analyzes the spatial characteristics of the Mount Lawu hiking trail via Cemoro Sewu based on Geographic Information Systems (GIS) by utilizing National Digital Elevation Model (DEMNAS) data. The methods used include the collection of trail and hiking post point data, spatial processing using QGIS, overlay with DEMNAS, as well as elevation profile extraction and slope analysis. The results of the study show that the hiking trail has an elevation range from approximately 1,913 meters to approximately 3,229 meters above sea level with a total elevation gain of around 1,316 meters. Elevation profile analysis shows a gradual increase pattern, with the most significant segment being from Post 1 to Post 2, which has the highest elevation gain. In addition, slope analysis results show that the trail is dominated by moderate to steep slope classes (approximately 23°–30°), especially in the middle sections up to near the summit. The spatial information produced in the form of route maps, elevation profiles, and slope distribution is able to provide a quantitative picture of the difficulty level of the route.Keywords: Geographic information system; Demnas; Hiking trails; Elevation profile; Mount Lawu AbstrakPenelitian ini menganalisis karakteristik spasial jalur pendakian Gunung Lawu via Cemoro Sewu berbasis Sistem Informasi Geografis (SIG) dengan memanfaatkan data Digital Elevation Model Nasional (DEMNAS). Metode yang digunakan meliputi pengumpulan data jalur dan titik pos pendakian, pengolahan spasial menggunakan QGIS, overlay dengan DEMNAS, serta ekstraksi profil elevasi dan analisis kemiringan lereng. Hasil penelitian menunjukkan bahwa jalur pendakian memiliki rentang elevasi dari ±1.913 mdpl hingga ±3.229 mdpl dengan total kenaikan elevasi sekitar ±1.316 meter. Analisis profil elevasi menunjukkan pola kenaikan bertahap dengan segmen paling signifikan berada pada Pos 1–Pos 2 sebagai bagian dengan kenaikan elevasi tertinggi. Selain itu, hasil analisis kemiringan lereng menunjukkan bahwa jalur didominasi oleh kelas lereng sedang hingga curam (±23°–30°), terutama pada bagian tengah hingga mendekati puncak. Informasi spasial yang dihasilkan berupa peta jalur, profil elevasi, dan distribusi kemiringan lereng mampu memberikan gambaran kuantitatif mengenai tingkat kesulitan jalur. 
PREDIKSI PENJUALAN SEPEDA MOTOR TERLARIS DI ASTRA HONDA BREBES MENGGUNAKANALGORITMA NAIVE BAYES Luthfi Ardyansyah; Nur Ariesanto Ramdhan; Puji wahyuningsih
Elkom: Jurnal Elektronika dan Komputer Vol. 19 No. 1 (2026): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v19i1.3756

Abstract

Penelitian ini bertujuan untuk menerapkan metode Support Vector Machine (SVM) dalam memprediksi tingkat penjualan sepeda motor di Astra Honda Brebes menggunakan data historis penjualan periode 2020–2025. Dataset penelitian terdiri dari 200 data penjualan yang memiliki atribut model motor, tahun kendaraan, harga, jenis transmisi, jenis kendaraan, kapasitas mesin, dan status penjualan. Proses penelitian meliputi preprocessing data, pembagian data training dan testing, proses klasifikasi menggunakan kernel RBF, serta evaluasi model menggunakan confusion matrix, accuracy, precision, recall, F1-score, AUC, dan cross validation. Hasil penelitian menunjukkan bahwa metode SVM mampu melakukan klasifikasi tingkat penjualan kendaraan dengan performa yang baik. Nilai accuracy yang diperoleh sebesar 89.50%, precision 87.20%, recall 85.40%, F1-score 86.29%, AUC sebesar 0.91, serta cross validation accuracy sebesar 91.20%. Hasil tersebut menunjukkan bahwa metode SVM memiliki kemampuan yang baik dalam membedakan kategori kendaraan laris dan kurang laris. Penelitian ini diharapkan dapat membantu pihak dealer dalam menentukan strategi pemasaran, pengelolaan stok kendaraan, dan pengambilan keputusan secara lebih efektif, objektif, dan berbasis data.
Optimasi Mobilenetv2 Dengan Transfer Learning Untuk Klasifikasi Penyakit Daun Cabai Rizkon Jajila; Nur Ariesanto Ramdhan; Puji Wahyuningsih; Bambang Irawan
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 1 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i1.33812

Abstract

This research effort seeks to establish a robust classification model for chili leaf diseases through optimization of the MobileNetV2 architecture using transfer learning methodology. The presence of diseases in chili plants is often a major barrier to agricultural productivity, necessitating the development of a rapid and accurate early detection system. The dataset used for this investigation includes six different leaf condition categories, specifically: Bacterial Spot, Cercospora Leaf Spot, Leaf Curl Virus, Healthy Leaf, Nutrient Deficiency, and White Spot. The investigation process begins with an image pre-processing phase and the application of data augmentation techniques, which aim to increase the variability of the training data while simultaneously reducing the risk of overfitting. Next, the model is trained using pre-trained weights from ImageNet, which are adjusted to align with the inherent visual characteristics of chili leaves. Model evaluation is conducted rigorously based on accuracy, precision, recall, and F1-score metrics. The experimental results demonstrate outstanding performance, with the model achieving an accuracy rate of 99%, an average F1-score of 0.99, and a validation loss of 0.07. These figures demonstrate the model's highly competent generalization ability when applied to new data. Analysis facilitated by a confusion matrix found a very low error rate, with only 11 images (0.73%) misclassified out of a total of 1,500 test images. These results support the assertion that MobileNetV2 optimization is highly efficient and accurate in identifying chili leaf diseases. This model has significant potential for integration into mobile devices or digital image-based smart farming systems, thereby assisting farmers in making informed decisions in real time.
PENGEMBANGAN SISTEM PENDUKUNG KEPUTUSAN MENGGUNAKAN METODE MOORA UNTUK PEMILIHAN OLI MOTOR MATIC Rengga Hanif Habib Pradipta; Otong Saiful Bachri; Nur Ariesanto Ramdhan
JUTECH : Journal Education and Technology Vol 6, No 2 (2025): JUTECH DESEMBER
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v6i2.5227

Abstract

Oli motor matic memiliki peranan penting dalam menjaga performa dan daya tahan mesin, namun banyak mahasiswa masih belum memaham pentingnya pemilihan oli yang sesuai. Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan pemilihan oli motor matic terbaik dengan menggunakan metode Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA). Metode ini mampu menangi berbagai kriteria seperti harga, kualitas, kekentalan, tahun produksi, dan ukuran oli. Data dikumpulkan melalui observasi, wawancara, dan studi literatur. Sistem dikembangkan  menggunakan pendekatan rekayasa perangkat lunak model waterfall dan divisualisasikan dengan Unified Modeling Language (UML) untuk mendukung perencanaan sistem. Perhitungan metode MOORA dilakukan secara manual dan diolah menggunakan Microsoft Axcel, menghasilkan pemeringkatan alternatif oli terbaik berdasarkan nilai preferensi. Hasil penelitian menunjukkan bahwa metode MOORA efektif dalam menyeleksi oli berdasarkan banyak kriteria secara objektif dan sistematis. Sistem yang dikembangkan dapat membantu mahasiswa dalam menentukan pilihan oli motor matic yang sesuai dengan kebutuhan dan spesifikasi kendaraan, serta memberikan solusi berbasis teknologi dalam proses pengambilan keputusan. Kesimpulannya, penerapan metode MOORA dalam sistem pendukung keputusan terbukti mampu memberikan hasil yang akurat dan dapat diandalkan.
IMPLEMENTASI METODE AHP PADA SISTEM PENDUKUNG KEPUTUSAN GURU BERPRESTASI DI SMK Selvi Awaliyah; Nur Ariesanto Ramdhan; Puji Wahyuningsih
JUTECH : Journal Education and Technology Vol 6, No 2 (2025): JUTECH DESEMBER
Publisher : STKIP Persada Khatulistiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/jutech.v6i2.5201

Abstract

Penilaian guru berprestasi di lingkungan sekolah merupakan proses yang penting namun sering kali menghadapi tantangan subjektivitas dan ketidakteraturan dalam pengambilan keputusan. Penelitian ini bertujuan untuk merancang dan membangun sistem pendukung keputusan (SPK) berbasis metode Analytical Hierarchy Process (AHP) guna membantu proses pemilihan guru berprestasi secara objektif dan terstruktur di SMK Ma’arif NU 01 Ketanggungan. Teknik pengumpulan data pada penelitian ini dengan memberikan kuisioner kepada siswa agar penilaian lebih objektif.  Metode AHP digunakan untuk membobotkan kriteria penilaian yang meliputi kompetensi pedagogik, profesional, kepribadian, dan sosial. Sistem Pendukung Keputusan yang dihasilkan dalam penelitian ini dapat menampilkan peringkat guru yang sesuai dengan penilaian yang dilakukan dengan menggunakan metode Analytical Hierarchy Process (AHP). Dengan adanya sistem ini, proses pemilihan guru berprestasi menjadi lebih transparan, efisien, dan dapat dipertanggungjawabkan.
Co-Authors Andin Ayu Oksilia Ramadhani Bambang Irawan Luthfi Ardyansyah Otong Saeful Bachri Otong Saiful Bachri Puji Wahyuningsih Rengga Hanif Habib Pradipta Rizkon Jajila Selvi Awaliyah Teuku Rizal Adi Pangestu