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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Pro Bisnis JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JISPO (Jurnal Ilmu Sosial dan Ilmu Politik) Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Journal Technology and Implementation Bussines The IJICS (International Journal of Informatics and Computer Science) JURTEKSI Jurnal Teknologi dan Informasi Infoman's: Jurnal Ilmu-Ilmu Manajeman dan Informatika Management Studies and Entrepreneurship Journal (MSEJ) JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia) Journal of Computer Science and Engineering (JCSE) Journal of Terrorism Studies Journal of Innovation in Community Empowerment (JICE) Indonesian Journal of Networking and Security - IJNS Jurnal Pengabdian Mitra Masyarakat (JPMM) JURNAL RETENTUM Jurnal Penelitian Pendidikan Indonesia ILKOMEDIA: Jurnal Ilmu Komputer dan Multimedia
Rizki Wahyudi
Universitas Amikom Purwokerto
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Enhancing Potato Leaf Disease Detection: Implementation of Convolutional Vision Transformers with Synthetic Datasets from Stable Diffusion Astuti, Tri; Umar, Amri Nurkholis; Wahyudi, Rizki; Rifai, Zanuar
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2167

Abstract

Numerous studies have addressed the classification of potato plants. However, the available datasets often lack the necessary diversity to improve the accuracy of predictive classification models effectively. Our research capitalizes on synthetic datasets generated through the Stable Diffusion 1.5 image generation method to address this challenge. This study suggests a new way to solve the problem by using artificial datasets created with the Stable Diffusion 1.5 method to teach a Convolutional Vision Transformer (CvT) model how to identify diseases on potato leaves accurately. Our objective is to train the CvT model employing synthetic datasets to excel in detecting potato leaf diseases. Our methodology encompasses the model's training using synthetic datasets from Stable Diffusion 1.5. We employ a comprehensive dataset of 11,121 synthetic images to train the Convolutional Vision Transformer (CvT) model, which enables it to accurately identify various potato leaf diseases such as black leg/soft rot, mosaic, leaf roll, early blight, and late blight. We conduct evaluations at multiple training stages to gauge the model's performance and accuracy. The outcomes of our research underscore the effectiveness of employing synthetic datasets from Stable Diffusion 1.5, which significantly augments the available image data while preserving a high level of accuracy. The CvT model proficiently identifies potato leaf diseases with an evaluation accuracy of 84%. Additional testing reveals that by the fifth epoch, the CvT model attains an accuracy of 81% when assessed using 82 randomly selected images of diseased plants from Google. The implications of this research are far-reaching, particularly within the domains of image processing and agriculture. The strategy of utilizing synthetic datasets to train the CvT model presents an efficient remedy to address the limitations of original image datasets. The adept disease detection capability of the CvT model holds the potential to expedite plant condition identification, mitigate crop loss, and ultimately amplify agricultural productivity. This study effectively demonstrates that the Convolutional Vision Transformer (CvT), when leveraged with synthetic datasets from Stable Diffusion 1.5, produces a model capable of accurately identifying potato leaf diseases. These findings bear positive implications for both the agricultural and image-processing sectors. 
SISTEM INFORMASI GEOGRAFIS (SIG) PEMETAAN BENCANA ALAM KABUPATEN BANYUMAS BERBASIS WEB Wahyudi, Rizki; Astuti, Tri
Jurnal Teknologi dan Informasi (JATI) Vol 9 No 1 (2019): Jurnal Teknologi dan Informasi (JATI)
Publisher : Program Studi Sistem Informasi, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (585.564 KB) | DOI: 10.34010/jati.v9i1.1448

Abstract

Banyumas merupakan salah satu kabupaten yang berada di Jawa Tengah, terdiri dari 27 kecamatan, 301 desa dan 30 kelurahan. Jumlah kejadian bencana yang tercatat di Badan Nasional Penanggulangan Bencana (BNPB) untuk kabupaten banyumas sebanyak 250, dengan meninggal dan hilang 31 jiwa, luka-luka 35 jiwa, menderita dan mengungsi 446.697 jiwa, rumah rusak 2000 unit, rumah terendam 50.779 unit, fasilitas pendidikan 169 unit, fasilitas peribadatan 110 unit. Urutan tiga teratas bencana yang menjadi penyebab adalah banjir, longsor, banjir dan tanah longsor Sistem Informasi Geografis (SIG) merupakan salah satu teknologi yang membantu mengelola, menyimpan, melakukan pemrosesan, analisis dan menampilkan data terkait geografis dalam kaitannya penelitian ini berguna untuk memetakan daerah rawan bencana alam dan menampilkan statistik perbandingan jumlah bencana alam yang ada di kabupaten banyumas. Pengujian dilakukan menggunakan metode Black-box Testing hasilnya Fungsional sistem dapat berfungsi dengan baik.
Strengthening Cooperation among Intelligence Agencies in the Enforcement of Law on Terrorism: The Case of Indonesia Wahyudi, Rizki; Syauqillah, Muhammad
JISPO Jurnal Ilmu Sosial dan Ilmu Politik Vol. 12 No. 1 (2022): JISPO Vol 12 No 1 2022
Publisher : Faculty of SociaI and Political Sciences (FISIP), Universitas Islam Negeri (UIN) Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/jispo.v12i1.14370

Abstract

The development of global, regional and national situations related to terrorism is dynamic and complex affecting national stability and security. It requires optimal and effective action from the government. The complexity of counter-terrorism requires the synergy of all parties. Strengthening cooperation between intelligence agencies can solve the current threat of terrorism. This article aims to propose intelligence cooperation and present the concept of intelligence synergy within the framework of law enforcement in combating terrorism in Indonesia. The article is descriptive qualitative research. It employs in-depth interviews and literature study as a data-gathering technique. Using collaborative governance theory, the article argues that to realize synergy between intelligence agencies, all Indonesian intelligence agencies need to conduct joint action procedures and hold joint terrorism countermeasures exercises, and related activities aimed at creating a common perception and eliminating rivalry among the agencies.
OPTIMIZATION OF CART ALGORITHM BASED ON ANT BE COLONY FEATURE SELECTION FOR STUNTING DIAGNOSIS Subarkah, Pungkas; Ikhsan, Ali Nur; Wahyudi, Rizki; Rofiqoh, Dayana
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 2 (2025): Maret 2025
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i2.3579

Abstract

Abstract: One of the main health problems in children is stunting which is one of the concerns in the Sustainable Development Goals (SDGs). Specifically in Indonesia, the prevalence of stunting in 2024 is 21.6%. This figure is still relatively high, because the target prevalence of stunting is 14%. This study aims to implement machine learning knowledge through the Classification And Regression Trees (CART) algorithm based on Ant Be Colony (ABC) feature selection which aims to determine the increase in accuracy in analyzing stunting datasets. The data used comes from Kaggle which consists of 16500 datasets. The dataset consists of gender, age, birth length, birth weight, body length, body weight, breastfeeding and stunting status. The research methods used are data collection, data preprocessing, classification, and evaluation using K-fold cross validation. The results obtained in this research are the implementation of the CART algorithm obtained a value of 89.86% and the results of CART with Ant Be Colony (ABC) feature selection, which obtained an accuracy value of 93.65%. This shows that there is an increase in the accuracy value in the use of CART algorithm optimization and Ant Be Colony (ABC) feature selection by 3.76%. With the research results that have been obtained, it can be categorized as excellent accuracy value excellent. It is hoped that further research can be carried out by adding other classification algorithms or adding feature selection.            Keywords: classification; feature selection; optimazation; stunting Abstrak: Salah satu masalah kesehatan utama pada anak adalah stunting yang menjadi salah satu perhatian dalam Sustainable Development Goals (SDGs). Khusus di Indonesia angka Pravelensi stunting pada tahun 2024 di angka 21.6%. Angka ini masih tergolong tinggi, karena target angka pravelensi stunting ialah 14%. Penelitian ini bertujuan untuk mengimplementasikan pengetahuan machine learning melalui algoritma Classification And Regression Trees (CART) berbasis seleksi fitur Ant Be Colony (ABC) yang bertujuan untuk mengetahui peningkatan akurasi dalam menganalisis dataset stunting. Data yang digunakan bersumber dari Kaggle yang terdiri dari 16500 dataset. Dataset terdiri dari jenis kelamin, usia, panjang lahir, berat lahir, panjangg badan, berat badan, menyusui dan status stunting.  Metode penelitian yang digunakan adalah pengumpulan data, preprocessing data, klasifikasi, dan evaluasi menggunakan K-fold cross validation. Hasil yang diperoleh pada penelitian ini adalah Implementasi algoritma CART memperoleh nilai sebesar 89,86% dan hasil seleksi fitur CART dengan Ant Be Colony (ABC) memperoleh nilai akurasi sebesar 93,65%. Hal ini menunjukkan adanya peningkatan nilai akurasi pada penggunaan optimasi algoritma CART dan pemilihan fitur Ant Be Colony (ABC) sebesar 3,76%. Dengan hasil penelitian yang telah diperoleh dapat dikategorikan nilai akurasi yang diperoleh sangat baik. Diharapkan dapat dilakukan penelitian selanjutnya dengan menambahkan algoritma klasifikasi lain atau menambahkan seleksi fitur. Kata kunci: klasifikasi; optimalisasi; seleksi fitur; stunting
Analisis Strategi Pemasaran Dan Penjualan Sepeda Motor Bekas Pada Showroom Pak H. Karya Wahyudi, Rizki; Suherman, Enjang; Khalida, Laras Ratu
Management Studies and Entrepreneurship Journal (MSEJ) Vol. 6 No. 4 (2025): Management Studies and Entrepreneurship Journal (MSEJ)
Publisher : Yayasan Pendidikan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/msej.v6i4.8953

Abstract

Showroom Pak H. Karya mengalami penurunan penjualan sepeda motor bekas pada 2021–2023 di tengah persaingan ketat industri otomotif. Penelitian ini bertujuan untuk menganalisis strategi pemasaran yang diterapkan guna meningkatkan penjualan. Metode penelitian yang digunakan adalah kualitatif dengan desain penelitian studi kasus. Pengumpulan data dilakukan melalui wawancara dan angket, sedangkan analisis data menggunakan metode SWOT untuk mengidentifikasi faktor internal dan eksternal showroom. Hasil penelitian menunjukkan bahwa showroom memiliki keunggulan dalam variasi produk, harga kompetitif, dan pelayanan yang baik, namun terkendala dalam promosi, lokasi yang kurang strategis, dan manajemen stok yang belum optimal. Untuk meningkatkan penjualan, showroom perlu mengoptimalkan pemasaran digital, memperluas strategi promosi, serta membangun kerja sama dengan mitra bisnis. Dengan strategi yang tepat, showroom dapat mempertahankan daya saing dan meningkatkan volume penjualan secara berkelanjutan.
Hyperband‑Optimized LightGBM and Ensemble Learning for Web Phishing Detection with SHAP‑Based Interpretability Wahyudi, Rizki
Journal of Computer Science and Engineering (JCSE) Vol 6, No 2: August (2025)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study evaluates the performance of three tree boosting algorithms, Random Forest (RF), XGBoost (XGB), and LightGBM (LGBM), in detecting phishing websites using a phishing dataset based on HTML, URLs, and network features. Two hyperparameter optimization strategies were tested: Hyperband search (HalvingRandomSearchCV) and stacking ensemble combining all three models. The evaluation was conducted based on five main metrics: accuracy, precision, recall, F1-score, and AUC‑ROC. The results indicate that LightGBM tuned via Hyperband achieved the highest performance (accuracy 0.9724; AUC‑ROC 0.9702), followed by ensemble tuned (accuracy 0.9697; AUC‑ROC 0.9684). SHAP analysis was used to interpret the contribution of key features in predicting phishing websites. The AUC‑ROC difference of 0.0034 points from the XGBoost baseline (0.9668) confirms the effectiveness of Hyperband tuning and stacking ensembles for phishing detection
Co-Authors Aep Saprul Mujahid Agus Junadi Agus Junaidi Andi Saryoko Andik Wijanarko Berlilana - Budi Artono Cahya Giwangkara Yuliawan Chairiza, Wahyu Damar Rivaldi Zulkarnaen Desty Rakhmawati Dewi Novianti Dewi Rosanti, Dewi Dhanar Intan Surya Dian Mutiara Donna Setiawati Dwi Warsari, Dwi Elfandy Cahya Pradana Ely Purnawati Fiby Nur Afiana Gilang Kusumawardana Gustin Setyaningsih Haryani Hidetaka Nambo Ikhsan, Ali Nur Imam Tahyudin Khalida, Laras Ratu Kuat Indartono Lussy Dwi Oktaviana Luzy Oktaviany Marmaini, Marmaini Muhammad Iqbal Muliasari Pinilih Noto Setyo Putro Nugroho, Widya Nurfaizal, Yusmedi Prasetyo, Wisnu Dwi Primandani Arsi Priyanto, Sapto, Mr. Rahmita Pratama Raul Satria Retno Waluyo Rhinaldi, Krisna Rhinaldi, Krisna Rifai, Zanuar Rofiqoh, Dayana Rudy Yaniar Sianida Ruly Niko Dwi Putra S.M Santi Winarsih Sarmini - Sarmini Subarkah, Pungkas Suherman, Enjang Syauqillah, Muhammad Tri Astuti Tri Astuti Tri Astuti Tri Astuti Tri Purwanto, Tri Trimin Kartika, Trimin Umar, Amri Nurkholis Wiga Maulana Wisnu Dwi Prasetyo