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All Journal Elkom: Jurnal Elektronika dan Komputer Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik INFOKAM IKRA-ITH Informatika : Jurnal Komputer dan Informatika JMM (Jurnal Masyarakat Mandiri) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Ilmiah Intech : Information Technology Journal of UMUS Infotek : Jurnal Informatika dan Teknologi JUTECH : Journal Education and Technology Journal of Education Technology Information Social Sciences and Health Jurnal Sintaks Logika (JSilog) Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Journal of Exploratory Dynamic Problems
Nur Ariesanto Ramdhan
Prodi Teknik Informatika, Universitas Muhadi Setiabudi
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Neuroscience Nursing Public Health Social Sciences Other

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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.
Pemodelan Analisis Sentimen Ulasan Pengguna Aplikasi Info Bmkg Menggunakan Pendekatan Multinomial Naïve Bayes Moh. Syaogi; Nur Ariesanto Ramdhan; Otong Saeful Bachri; Bambang Irawan
Jurnal Sintaks Logika Vol. 6 No. 1 (2026): Januari 2026
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/jsilog.v6i1.4285

Abstract

Info BMKG is one of several digital platforms that have been pushed by the fast evolution of IT to replace traditional methods of providing public services. Reviews on the Play Store can be used to determine user perceptions and levels of satisfaction with the application. Manual analysis is laborious and inefficient due to the high number of evaluations. Consequently, the purpose of this research is to use the Naive Bayes algorithm to categorize evaluations of the Info BMKG app as either positive or negative in order to do sentiment analysis. Using a web scraping approach, a total of 5,000 user evaluations were obtained for the study data. Next, the data underwent text preprocessing, word weighting using the TF-IDF technique, and sentiment classification with the Multinomial Naive Bayes algorithm. There was an 80:20 split between the dataset's training and testing sets. The experimental findings show that the Naive Bayes algorithm achieves an accuracy of 87.83% on the testing data when it comes to classifying user review emotions.
PELATIHAN DAN PENDAMPINGAN PEMBUATAN MEDIA PEMBELAJARAN BERBASIS AUGMENTED REALITY PADA GURU-GURU SMP Prasetyo Yuli Kurniawan; Atikah Mumpuni; Nur Ariesanto Ramdhan
JMM (Jurnal Masyarakat Mandiri) Vol 8, No 6 (2024): Desember
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v8i6.27100

Abstract

Abstrak: Augmented reality sebagai media yang mengintegrasikan teknologi, belum banyak digunakan oleh guru-guru di SMP Negeri 2 Jatinegara. Hal ini dilatarbelakangi karena kurangnya pemahaman dan keterampilan guru dalam membuat media berbasis augmented reality. Tujuan kegiatan pengabdian kepada masyarakat ini adalah untuk meningkatkan pemahaman dan keterampilan guru dalam membuat media berbasis augmented reality. Mitra dalam kegiatan ini yaitu guru-guru di SMP Negeri 2 Jatinegara dengan jumlah 25 guru. Pengukuran pengetahuan dan keterampilan guru-guru tentang augmented reality dan assemblr studio digunakan pretest dan posttest. Berdasarkan hasil pretest dan posttest dapat disimpulkan bahwa adanya peningkatan pemahaman dan keterampilan guru dalam membuat media pembelajaran berbasis augmented reality. Masing-masing peningkatan sebessar 52,58% untuk pemahaman mengenai augmented reality, 51,42% untuk pemahaman mengenai software assemblr studio, serta 57% untuk peningkatan keterampilan membuat media berbasis augmented reality. Saran-saran yang dapat diberikan yaitu guru-guru dapat menggunakan laptop dalam membuat Augmented Reality. Selain itu, pembuatan Augmented Reality harus menyesuaikan capaian pembelajaran pada tiap-tiap mata pelajaran.Abstract: Augmented Reality as a media integrating technology, is not widely used by teachers at SMP Negeri 2 Jatinegara. This is due to the lack of understanding and skills among teachers in creating augmented reality-based media. The objective of this community service activity is to improve teachers' understanding and skills in developing augmented reality-based media. The participants in this activity were teachers from SMP Negeri 2 Jatinegara, totaling 25 teachers. The assessment of teachers' knowledge and skills regarding augmented reality and Assemblr Studio was conducted through pretests and posttests. Based on the results of the pretests and posttests, it can be concluded that there was an increase in teachers' understanding and skills in creating augmented reality-based learning media. Each aspect showed an increase of 52.58% in understanding of augmented reality, 51.42% in understanding of Assemblr Studio software, and 57% in skills for developing augmented reality-based media. Recommendations include that teachers use laptops to create augmented reality. Additionally, the development of augmented reality should align with the learning outcomes of each subject.
Perbandingan Kinerja Multinomial Naïve Bayes pada Variasi Rasio Data Training dan Testing dalam Analisis Sentimen Ulasan Taman Rakyat Slawi Ayu Moh. Syaogi; Nur Ariesanto Ramdhan; Otong Saeful Bachri
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.6941

Abstract

Sentiment analysis is one of the text mining techniques used to identify public opinions toward a particular object or service. Taman Rakyat Slawi Ayu, as one of the public green spaces in Tegal Regency, receives numerous visitor reviews through Google Maps, which can be utilized as a source of information to evaluate the quality of its services and facilities. This study aims to compare the performance of the Multinomial Naïve Bayes algorithm using different training and testing data split ratios for sentiment analysis of Taman Rakyat Slawi Ayu reviews. The data were collected through a web crawling technique on Google Maps, resulting in 4,153 reviews. After the data selection process, 1,794 reviews met the analysis criteria. The preprocessing stage consisted of cleaning, case folding, tokenizing, stopword removal, and stemming, resulting in 1,789 reviews ready for classification. Sentiment labeling was performed based on user ratings, where ratings of 1–3 were categorized as negative sentiment and ratings of 4–5 as positive sentiment. The experiments were conducted using three data split ratios: 70:30, 80:20, and 90:10. The results showed that the 90:10 ratio achieved the highest accuracy of 87.71%, with a precision of 0.88, recall of 0.99, and F1-score of 0.93. However, this ratio used a smaller testing dataset, making the evaluation results less representative than those obtained using the other data split ratios. Overall, the study demonstrates that variations in the training and testing data split ratio affect the classification performance of the Multinomial Naïve Bayes algorithm in sentiment analysis of Taman Rakyat Slawi Ayu reviews.
Penerapan Metode K-Means pada Pengelompokan Persebaran Kasus Diabetes Mellitus di Kecamatan Kersana Muhumatul Ifadah Ifadah; Agyztia Premana; Nur Ariesanto Ramdhan
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7257

Abstract

Diabetes Mellitus is a non-communicable disease that requires attention in public health prevention and control efforts. The distribution of cases varies across villages, requiring an analytical method to identify regional groups based on case levels. This study aims to apply the K-Means Clustering method to classify the distribution of Diabetes Mellitus cases in 13 villages in Kersana District. The data consist of 365 cases, comprising 82 male cases and 283 female cases. This study uses a descriptive quantitative approach, with the total number of Diabetes Mellitus cases as the main clustering variable, while gender data are used as supporting descriptive information. The clustering process divides the villages into three clusters: low, medium, and high. The results show that the low-level cluster consists of 9 villages with an average of 15.33 cases, the medium-level cluster consists of 2 villages with an average of 36 cases, and the high-level cluster consists of 2 villages with an average of 77.50 cases. Limbangan and Kradenan are categorized as high-level areas, Cikandang and Kersana as medium-level areas, while Cigedog, Kubangpari, Ciampel, Jagapura, Sutamaja, Kemukten, Sindangjaya, Pande, and Keramatsampang are categorized as low-level areas. The evaluation using the Silhouette Score resulted in a value of 0.690, indicating a reasonably good cluster structure. The results demonstrate that K-Means can help identify regional groups based on Diabetes Mellitus case levels and provide supporting information for prioritizing health monitoring and planning health programs.
Model Deep Convolutional Neural Network Untuk Klasifikasi SPAM Berbasis Teks Gesarsy Giova Ditya Illiyyin; Agyztia Premana; Nur Ariesanto Ramdhan; bambang irawan
Journal of Exploratory Dynamic Problems Vol. 3 No. 1 (2026): Vol.3 No.1 2026
Publisher : Fakultas Keguruan dan Ilmu Pendidikan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/edp.v3i1.215

Abstract

Pesan spam berbasis teks tidak lagi bersifat tunggal, melainkan terbagi ke dalam beberapa kategori dengan tingkat risiko yang berbeda, misalnya promosi, phishing/penipuan akun, pinjaman online ilegal, dan undian berhadiah palsu, sehingga klasifikasi biner (spam/bukan spam) dinilai kurang memadai untuk mendukung penanganan yang tepat sasaran. Penelitian ini merancang dan menguji model Deep Convolutional Neural Network (CNN) untuk klasifikasi multi-kategori spam berbasis teks. Arsitektur model mengadopsi pendekatan multi-kernel CNN (kernel berukuran 3, 4, dan 5) di atas lapisan embedding kata, dilanjutkan dengan global max pooling dan lapisan fully connected dengan aktivasi softmax untuk lima kelas. Karena keterbatasan akses terhadap korpus spam berlabel nyata, pengujian purwarupa dilakukan pada dataset dummy (sintetis) sebanyak 3.000 sampel yang dibangkitkan secara terprogram dengan variasi leksikal, kesalahan ejaan, dan noise label untuk mendekati karakteristik data dunia nyata. Model dilatih menggunakan optimizer Adam dengan strategi early stopping berdasarkan validation loss. Evaluasi pada data uji menghasilkan akurasi sebesar 95.11%, precision rata-rata (macro) 95.11%, recall rata-rata (macro) 95.10%, dan mean Average Precision (mAP) sebesar 92.39%. Hasil ini menunjukkan bahwa arsitektur CNN multi-kernel yang diusulkan mampu membedakan kelima kategori pesan dengan baik pada data uji, meskipun validasi lanjutan pada korpus nyata tetap diperlukan sebelum model diterapkan pada sistem produksi.
Pengembangan Game Edukasi Matematika Berbasis Android pada Siswa SDN Siasem 03 Annisa Ayu Nindita; Agyztia Premana; Nur Ariesanto Ramdhan
SABER : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Vol. 3 No. 4 (2025): Oktober: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/saber.v3i4.3856

Abstract

Learning math story problems for second-grade students at Siasem 03 Elementary School still faces challenges due to the lack of interactive learning media. This study aims to develop an Android-based math education game using Unity as an engaging and easy-to-use learning medium. The research method used was the Waterfall method, with stages of analysis, design, implementation, testing, and maintenance. The results showed that the application was successfully developed and all features functioned well based on Black Box Testing. The user assessment obtained an average percentage of 95.48%, categorized as Very Interesting, making the application suitable for use as a learning medium. This educational game is expected to help students understand math story problems and increase their interest in learning.
Co-Authors Agyztia Premana Akhmad Pandhu Wijaya, Akhmad Pandhu Andin Ayu Oksilia Ramadhani Annisa Ayu Nindita Bambang Irawan Devi Adi Nufriana Dimas Wahyudi Elis Afriyani Gesarsy Giova Ditya Illiyyin Haries Anom SAN Irawan, Bambang Luthfi Ardyansyah M Alfin Alfaridzi Moh. Syaogi Moh. Syaogi Muhumatul Ifadah Ifadah Mumpuni, Atikah Otong Saeful Bachri Otong Saeful Bachri Otong Saiful Bachri Prasetyo Yuli Kurniawan Puji Wahyuningsih R.M.Herdian Bhakti Rengga Hanif Habib Pradipta Rifqoh Febriyanti Putri Rizkon Jajila Selvi Awaliyah Sri Hartati Teuku Rizal Adi Pangestu Tohayah Tohayah