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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Masyarakat Informatika MATEMATIKA JURNAL SISTEM INFORMASI BISNIS Journal of Informatics and Technology Jurnal Sains dan Teknologi Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) ComTech: Computer, Mathematics and Engineering Applications Jurnal Guru Dikmen & Diksus Indian Journal of Forensic Medicine & Toxicology Journal of Social Research Engagement: Jurnal Pengabdian Kepada Masyarakat
Aris Sugiharto
Program Studi Ilmu Komputer, Universitas Diponegoro, Semarang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

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ANALISIS POLA KEMIRINGAN TULISAN TANGAN UNTUK MENGIDENTIFIKASI KEPRIBADIAN SESEORANG MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM) Khabib Mustofa; Aris Sugiharto; Priyo Sidik Sasongko
Journal of Informatics and Technology Vol 2, No 3 (2013): Wisuda Agustus 2013
Publisher : Jurusan Ilmu Komputer / Informatika, FMIPA UNDIP, Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (639.055 KB)

Abstract

Grafologi merupakan ilmu pengetahuan yang digunakan untuk mengidentifikasi kepribadian seseorang melalui tulisan tangan. Salah satu fitur khusus yang dapat digunakan adalah melalui kemiringan tulisan tangannya. Aplikasi analisis pola kemiringan tulisan tangan ini dibangun menggunakan Support Vector Machine (SVM) yang terbagi menjadi dua tahapan yaitu tahap pelatihan dan tahap pengujian. Tahap pelatihan dilakukan untuk mendapatkan data pelatihan dalam bentuk file txt, sedangkan tahap pengujian dilakukan untuk melakukan pengklasifikasian sekaligus memberikan hasil berupa identifikasi kepribadian pengguna. Masukan sistem berupa file citra tulisan tangan dengan format bitmap, yang selanjutnya dilakukan proses preprocessing dan ekstraksi fitur. Pada tahap pelatihan, file citra dikategorikan ke dalam tiga kelas yaitu kelas miring kanan, miring kiri, dan tegak, sedangkan pada tahap pengujian, file citra mengalami proses klasifikasi yang melibatkan SVM sehingga didapatkan identifikasi kepribadian pengguna. Tahap pengujian dilakukan sebanyak 5 kali menggunakan 90 data yang dilakukan dengan dua skenario. Skenario pertama menggunakan jumlah data yang sama antara data pelatihan dan pengujian, menghasilkan persentase keberhasilan dengan rata-rata 92,89%, sedangkan skenario kedua yang menggunakan jumlah data yang berbeda antara data pelatihan dan pengujian, menghasilkan persentase keberhasilan dengan rata-rata 92,44%
The Effectiveness of the Revitalization of “Jogo Tonggo” as Local Wisdom in Vigilance and Prevention of Transmission of COVID-19 in Central Java Province Yulianto Prabowo; Agus Suwandono; Bagoes Widjanarko; Sutopo Patriajati; Aris Sugiharto
Indian Journal of Forensic Medicine & Toxicology Vol. 16 No. 3 (2022): Indian Journal of Forensic Medicine and Toxicology
Publisher : Institute of Medico-legal Publications Pvt Ltd

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37506/ijfmt.v16i3.18315

Abstract

Background: Jogo tonggo is an empowerment effort to increase community participation in preventing thetransmission and spread of Covid-19 in Central Java. The study aims to assess the effectiveness of the revitalizationof “jogo tonggo” as local wisdom toward increasing community knowledge, attitudes, and behavior towardCOVID-19.Methods: This study is true-experimental study involving 352 respondents, which are divided into the experimentalgroup and the control group. The used statistical analyses were paired simple t-test, independent t-test, Mann-Whitney, and Wilcoxon.Results: There was an increase in the mean after intervention between group 1 and group 2 including knowledgeof “jogo tonggo” (δ=2.39; p=0.001), knowledge of COVID-19 symptoms and transmission modes (δ=2.87; p=0.001),knowledge of prevention methods of COVID-19 (δ=1.63; p=0.001), attitudes towards “jogo tonggo” (δ=2.00;p=0.004), attitudes towards COVID-19 (δ=1.58; p=0.011), and COVID-19 prevention behavior (δ=6.04; p=0.001).Conclusion: The revitalization of “jogo tonggo” can increase knowledge, attitudes, and behavior toward COVID-19.
Perintisan Taman Baca Tunas Merapi sebagai Upaya Pengembangan Generasi Gemar Membaca di Dusun Druwak Desa Logede Kecamatan Karangnongko Klaten Eko Didik Widianto; Wahyu Krisna Hidayat; Aris Sugiharto
Engagement: Jurnal Pengabdian Kepada Masyarakat Vol 2 No 1 (2018): May 2018
Publisher : Asosiasi Dosen Pengembang Masyarajat (ADPEMAS) Forum Komunikasi Dosen Peneliti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/engagement.v2i1.25

Abstract

Reading Park in the community is one of the important instruments to support community's need for information and knowledge. This dedication activity aimed to pioneer the development of Reading Park in the hamlet of Druwak. This Reading Park contained a collection of books that could be accessed by the public and had been classified based on its subject. This Reading Park was expected to help the community, especially children and parents in Druwak hamlet, to realize the community of Druwak who like reading, so as to open the perception and orientation of thought towards literacy education.
Detection of chronic kidney disease using binary whale optimization algorithm Sutikno, Sutikno; Kusumaningrum, Retno; Sugiharto, Aris; Arif Wibawa, Helmie
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1511-1518

Abstract

Chronic kidney disease (CKD), a medical illness, is characterized by a steady deterioration in kidney function. A disease's ability to be prevented and effectively significantly treated depends on early diagnosis. The addition of filter feature selection to the machine learning algorithm has been done to detect CKD. However, the quality of its feature subset is not optimal. Wrapper feature selection can improve the quality of these feature subsets. Therefore, we proposed wrapper feature selection and binary whale optimization algorithm (BWOA) to enhance the accuracy of early CKD detection. We also make data improvements to improve accuracy, namely the preprocessing process with the median and modus techniques. We used a public dataset of 250 medical records of kidney sufferers and 150 completely healthy people. There are 24 features in this dataset. The test results showed that adding BWOA feature selection can increase accuracy. The proposed method produced an accuracy of 100%. Further research on these methods can be used to develop expert systems for early detection of CKD.
Improved car detection performance on highways based on YOLOv8 Sutikno, Sutikno; Sugiharto, Aris; Kusumaningrum, Retno; Wibawa, Helmie Arif
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.8031

Abstract

Car detection on the road through computer vision is crucial for improving safety, as it plays an essential role in spotting nearby vehicles and preventing fatal accidents. Additionally, car detection significantly contributes to the advancement of autonomous vehicles. Previous explorations of car detection using YOLOv5 have revealed weaknesses regarding its resulting mean average precision (mAP). This scenario led to the development of a more advanced version of you only look once (YOLO), namely YOLOv8. Consequently, this study aimed to adopt YOLOv8 for automatic car detection on the road. YOLOv8 is proven to perform better than the previous version. A dataset comprising video frame images was captured on the highway in Semarang, Indonesia. The experiment results indicated that the proposed approach achieved impressive precision, recall, and mAP values, reaching 94.1%, 98.2%, and 98.8%, respectively. The proposed approach enhanced mAP and training time when compared with YOLOv5. Therefore, it was concluded that the proposed method was better suited for real-time car detection.
Analisis Pengaruh Kualitas Website Shopee Terhadap Kepuasan Pengguna Berdasarkan Metode WebQual 4.0 (Studi Kasus : Mahasiswa Pengguna Shopee di Universitas Diponegoro) Adieb, M. Risqi Amirul; Sugiharto, Aris; Adhy, Satriyo
Jurnal Masyarakat Informatika Vol 15, No 1 (2024): May 2024
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.15.1.59737

Abstract

Maraknya pelaku bisnis e-commerce membuat persaingan jual beli online menjadi ketat sehingga e-commerce harus mampu mempertahankan keberadaannya. Salah satu faktor utama yang mendorong pelanggan melakukan kegiatan e-commerce adalah tingkat kepuasan pengguna dalam penerimaan website terhadap kualitas layanan website e-commerce tersebut. Penelitian ini bertujuan untuk menganalisis faktor kualitas website Shopee yang memengaruhi tingkat kepuasan pengguna dengan menggunakan metode WebQual 4.0 dan variabel tambahan user satisfaction. Hasil pengolahan dan analisis data didapatkan bahwa sumbangan pengaruh yang diberikan dimensi WebQual 4.0 secara bersama-sama terhadap user satisfaction dalam penerimaan website Shopee adalah 76,2%, yaitu dimensi WebQual 4.0 secara bersama-sama memiliki pengaruh yang signifikan terhadap user satisfaction, usability berpengaruh secara positif dan signifikan terhadap user satisfaction, information quality berpengaruh secara positif dan signifikan terhadap user satisfaction, dan service interaction quality berpengaruh secara positif dan signifikan terhadap user satisfaction.
Identifikasi Dini Curah Hujan Berpotensi Banjir Menggunakan Algoritma Long Short-Term Memory (Lstm) Dan Isolation Forest Wijayanto, Ahmad; Sugiharto, Aris; Santoso, Rukun
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 3: Juni 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.938718

Abstract

Curah hujan yang tinggi merupakan faktor utama yang dapat mengakibatkan banjir di suatu daerah. Pola curah hujan yang semakin tidak teratur dan peningkatan curah hujan ekstrem membuat pengendalian banjir semakin sulit. Identifikasi dini diperlukan untuk memahami peran curah hujan dalam manajemen sumber daya air dan perancangan infrastruktur air yang tangguh untuk daerah rawan banjir. Dengan keterbatasan data dan parameter input tunggal, model yang diusulkan menghadapi tantangan dalam forecasting pola curah hujan jangka panjang dan generalisasi data. Studi ini memproses data curah hujan BMKG untuk menghasilkan forecasting menggunakan Long Short-Term Memory (LSTM) berdasarkan pola data series dan hubungan jangka panjang. Algoritma Isolation Forest kemudian digunakan untuk mengidentifikasi secara otomatis curah hujan dengan potensi banjir. Probabilitas curah hujan tinggi diidentifikasi untuk menghitung ketahanan infrastruktur air dan menetapkan standar yang sesuai untuk daerah beriklim hujan dan rawan banjir. Prediksi LSTM dievaluasi menggunakan Mean Square Error (terbaik 19,11) dan Root Mean Square Error (terbaik 4,37) sebelum dilakukan forecasting jangka panjang. Model yang diusulkan bertujuan untuk membantu pemangku kepentingan secara cepat mengidentifikasi probabilitas curah hujan tinggi jangka panjang, khususnya di daerah Semarang.   Abstract High rainfall is a key factor causing floods in an area. Increasingly irregular rainfall patterns and rising extreme rainfall make it more challenging to control floods. Early identification is needed to understand rainfall's role in water resource management and designing resilient water infrastructure for flood-prone areas. With limited data and single input parameters, the proposed model faces challenges in long-term rainfall pattern forecasting and data generalization. This study processes BMKG rainfall data to generate forecasts using Long Short-Term Memory (LSTM) based on data series patterns and long-term relationships. The Isolation Forest algorithm is then used to automatically identify rainfall with flood potential. The probability of high rainfall is identified to calculate water infrastructure resilience and set appropriate standards for rainy, flood-prone areas. LSTM predictions are evaluated using Mean Square Error (best 19.11) and Root Mean Square Error (best 4.37) before conducting long-term forecasting. The proposed model aims to help stakeholders quickly identify the probability of long-term high rainfall, particularly in the Semarang area.
SKIN RASH CLASSIFICATION SYSTEM USING MODIFIED DENSENET201 THROUGH RANDOM SEARCH FOR HYPERPARAMETER TUNING Riyana Putri, Fayza Nayla; Isnanto, R.Rizal; Sugiharto, Aris
Jurnal Ilmiah Kursor Vol. 12 No. 4 (2024)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i4.418

Abstract

Skin rashes caused by various diseases, such as monkeypox, cowpox, chickenpox, measles, and HFMD, often present similar symptoms, making accurate diagnosis challenging. This study aims to improve the classification of skin diseases through the application of a modified DenseNet-201 architecture combined with hyperparameter optimization using Random Search. The base DenseNet-201 model, with pre-trained weights, was first tested, achieving an accuracy of 63%, with the highest performance in the Healthy and HFMD classes. The proposed modified model, optimized using Random Search, improved overall accuracy to 80%, with enhanced precision, recall, and F1-score across most classes. The model’s performance was particularly notable in the HFMD and normal skin classes, although further improvements are needed for challenging classes like Cowpox and Measles. The findings highlight the potential of Random Search for hyperparameter tuning to enhance the performance of deep convolutional neural networks in the medical image classification domain, offering a promising tool for efficient and accurate skin disease detection.
Classification of Real and Fake Images Using Error Level Analysis Technique and MobileNetV2 Architecture Baihaqi, Muhamad Nur; Sugiharto, Aris; Tantyoko, Henri
Jurnal Masyarakat Informatika Vol 16, No 1 (2025): May 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.1.73283

Abstract

Advancements in technology have made image forgery increasingly difficult to detect, raising the risk of misinformation on social media. To address this issue, Error Level Analysis (ELA) feature extraction can be utilized to detect error level variations in lossy-formatted images such as JPEG. This study evaluates the contribution of ELA features in classifying authentic and forged images using the MobileNetV2 model. Two scenarios were tested using the CASIA 2.0 dataset: without ELA and with ELA. Fine-tuning was performed to adapt the model to the new problem. Experimental results show that incorporating ELA improves model accuracy up to 93.1%, compared to only 76.41% in the scenario without ELA. Validation using k-fold cross-validation yielded a high average f1-score of 96.83%, confirming the effectiveness of ELA in enhancing the classification performance of authentic and forged images.
Optimizing VGG16 Architecture with Bayesian Hyperparameter Tuning for Tomato Leaf Disease Classification Arkan, Tsaqif Muhammad; Sugiharto, Aris; Wibawa, Helmie Arif
Jurnal Masyarakat Informatika Vol 16, No 2 (2025): Issue in Progress
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.2.73168

Abstract

This study proposes an optimized VGG16 architecture enhanced through Bayesian Optimization to improve the classification of tomato leaf diseases. The modified model integrates tunable parameters such as dropout rates, convolutional filters, and dense units, while maintaining the foundational structure of VGG16. To further refine performance, Bayesian Optimization is employed to search for the most effective combination of hyperparameters. Experiments conducted using the Tomato Leaf Disease Detection dataset demonstrate that the proposed method outperforms the original VGG16 model, achieving a test accuracy of 97.1% compared to 89.0%. These results underline the importance of architecture customization and systematic hyperparameter tuning for domain-specific deep learning tasks in agriculture.
Co-Authors Abd. Rasyid Syamsuri Adi Wibowo Adieb, M. Risqi Amirul Adiputera, Yusuf Fahmi Afry Rachmat Agus Suwandono Andi Gunawan Antariksa, Muhammad Deagama Surya Ari Wibawa Budi Santosa Arief Hidayat Arif Wibawa, Helmie Arkan, Tsaqif Muhammad Ary Setyadi Bagoes Widjanarko Baihaqi, Muhamad Nur Bayu Surarso Budi Warsito Budi Warsito Dedy Kurniawan Hadi Putra, Dedy Kurniawan Hadi Didit Suprihanto, Didit Eko Adi Sarwoko Eko Didik Widianto Eko Didik Widianto Eko Nur Hidayat Eko Prasetiawan Fajar Hari Prasetyo Fajar Nugraha Ganis Khufad Arridho Hanif Setiawan, Syariful Helmi Arif Wibawa Helmie Arif Wibawa Helmie Arif Wibawa Henny Indriyawati Hidayat, Agung Rahmad Ikhthison Mekongga Indriyati Indriyati Irfan Pradipta Juwanda, Farikhin Kamal Maulana Kushartantya Kushartantya Kusworo Adi Lusiana Kristiyanti Lutfi Rinanto Mochammad Hosam Muhammad Malik Hakim, Muhammad Malik Mustafid Mustafid Nazla Nurmila, Nazla Nikmah Rahmawati Pradhitya Nur Diyah S Pramudita Eka Hananto Prastio, Wahyu Tedi Priyo Sidik Sasongko R Rizal Isnanto Ragil Saputra Ragil Saputra Rahmat Gernowo Rambing, Danni Riyana Putri, Fayza Nayla Rizki Saputra, Naufal Roby Hanintyo Nursio Sakti Rukun Santoso Satriyo Adhy Sembiring, Rinawati Septya Maharani, Septya Sinta Tridian Galih Sugiyamto Sugiyamto, Sugiyamto suhartono, Suahrtono Sukmawati Nur Endah Supriyono Supriyono Suryo Hartanto Sutikno Sutikno Sutopo Patria Jati Tantyoko, Henri Tarno Tarno Toni Prahasto Victor Gayuh Utomo Wahyu Adi, Prajanto Wahyu Krisna Hidayat Wahyu Krisna Hidayat Wahyudi Setiawan widowati widowati Wijayanto, Ahmad Yudie Irawan Yulianto Prabowo