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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Pendidikan Teknologi dan Kejuruan Yustisia Jurnal EECCIS Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal Teknoinfo Jurnal Elektronika dan Telekomunikasi JUTIS : Jurnal Teknik Informatika EKONOMI BISNIS Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Jurnal Teknologi dan Sistem Informasi Jurnal Pendidikan dan Teknologi Indonesia KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Telematics and Information Technology (TELEFORTECH) Jurnal Informatika Polinema (JIP) Jurnal Rekayasa Perangkat Lunak Mitra Jurnal Pengabdian Masyarakat Multidisiplin (MJPMM)
Agus Wantoro
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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A New Approach of Steganography on Image Metadata Fernando, Yusra; Darwis, Dedi; Mehta, Abhishek R; Wamiliana, Wamiliana; Wantoro, Agus
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2110

Abstract

In this paper, we introduce a novel method, Steganography on Image Metadata (SIM), to tackle the problem of robustness modification in steganography.  The SIM method works by embedding messages into the metadata storage space of digital media. Metadata is information embedded in a file that explains the file's content. The advantage of this method is that it does not alter the pixel values in the image, ensuring no degradation in media quality, and the secret message remains secure even when robustness manipulations are applied to the stego-image. To enhance data security, this paper also suggests using Fernet cryptography for message encryption during the embedding process into the cover-image. According to experimental evaluations, the SIM technique can attain a maximum PSNR value of 100 dB and an outstanding MSE value of 0. All robustness manipulation issues in steganography can be effectively addressed using the SIM method. Test results demonstrate that the SIM method can withstand symmetric and asymmetric cropping manipulations down to a pixel size of 1x1, and the message can still be extracted. Testing with image rotation manipulation also proves that the message can be successfully extracted even when the stego-image is rotated up to 180 degrees. Experiments with image resizing manipulation also confirm that the message can be recovered even when the stego-image undergoes up to 90% compression. Testing with color effects applied to the image also does not affect message extraction results.
Desain Sistem Smart Charger Berbasis Fuzzy untuk Optimasi Pengisian Baterai Li-Ion Wantoro, Agus; Feriyanto, Dwi; Despa, Dikpride; Aminudin, Nur
Jurnal Informatika Polinema Vol. 12 No. 1 (2025): Vol. 12 No. 1 (2025)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v12i1.8110

Abstract

Perkembangan teknologi perangkat portabel dan kendaraan listrik menuntut sistem pengisian baterai yang efisien, aman, dan adaptif. Penelitian ini merancang dan mengimplementasikan sistem Smart Charger berbasis logika fuzzy untuk optimasi pengisian baterai lithium-ion (Li-Ion). Sistem mengintegrasikan sensor suhu dan tegangan dengan mikrokontroler untuk mengatur arus pengisian secara dinamis berdasarkan input parameter aktual seperti suhu baterai, tegangan awal, dan kapasitas sisa (State of Charge). Pengujian dilakukan pada berbagai skenario suhu lingkungan dan kondisi baterai. Hasil menunjukkan bahwa sistem fuzzy mampu memberikan kestabilan keluaran berdasarkan inputan yang diberikan dibandingkan dengan charger konvensional. Sistem akan menyesuaikan arus pengisian dengan kondisi baterai, khususnya pada suhu tinggi, tanpa intervensi manual. Penelitian ini membuktikan bahwa penerapan fuzzy logic dalam sistem pengisian baterai dapat memberikan peningkatan performa, keamanan, dan efisiensi. Sistem ini memiliki potensi untuk dikembangkan dan diterapkan pada berbagai perangkat elektronik serta dapat dikembangkan lebih lanjut dengan pendekatan adaptive rule learning dan integrasi antarmuka pemantauan real-time
Feature Selection and Class Imbalance Machine Learning for Early Detection of Thyroid Cancer Recurrence: A Performance-Based Analysis Wantoro, Agus; Caesarendra, Wahyu; Syarif, Admi; Soetanto, Hari
Jurnal Elektronika dan Telekomunikasi Vol 25, No 2 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.758

Abstract

Early detection of thyroid cancer recurrence is a crucial factor in patient survival and treatment effectiveness. Misdetection results in disease severity, high cost, recovery time, and decreased service quality. In addition, the main challenges in developing a Machine Learning (ML)-based detection decision support system are class imbalance in medical data and high feature dimensions that can affect model accuracy and efficiency. This study proposes a feature selection-based approach and class imbalance handling to improve the performance of early detection of Thyroid cancer. Several feature selection techniques, such as Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), and Chi-Square (CS), can select features based on weighted ranking. In addition, to overcome the imbalanced class distribution, we use the Synthetic Minority Over-Sampling Technique (SMOTE). ML classification models such as k-NN, Tree, SVM, Naive Bayes, AdaBoost, Neural Network (NN), and Logistic Regression (LR) are tested and evaluated based on a confusion matrix, including accuracy, precision, recall, time, and log loss. Experimental results show that the combination of imbalanced class handling strategies significantly improves the prediction performance of ML algorithms. In addition, we found that the combination of CS+NN feature selection techniques consistently showed optimal performance. This study emphasizes the importance of data pre-processing and proper algorithm selection in the development of a machine learning-based thyroid cancer detection system.
Fuzzy medical expert system for prediction of prostate cancer Wantoro, Agus; Rusliyawati, Rusliyawati; Sutyarso, Sutyarso; Hadibrata, Exsa
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1466-1477

Abstract

We developed the fuzzy medical expert system (F-MES) based on fuzzy inference system (FIS) Mamdani using a different approach to prostate cancer risk (PCR) prediction. The difference in our research is that we modify the membership function on the input variable according to medical standards. We used the same input variables as the previous study, namely age, prostate-specific antigen (PSA), prostate volume (PV), and percentage (%) free PSA (%FPSA). The data on the input variable is used as input into F-MES and displays the output in the form of a percentage (%) of PCR. If the PCR is >50%, then the patient is advised to undergo a biopsy test. We conducted an analysis with the doctor to create a simple domain and rule base of 24 rules. Our number of rules is lower than previous studies of 80 and 240, but our prediction results are better the F-MES evaluation used the same 56 patients, that the F-MES we developed had an accuracy of 857%. This score is better than previous studies of 75% and 76%. Our F-MES is simple but effective and can be used as a supporting tool in decision-making in medical diagnosis.
Analisis Sentimen Kesehatan Mental di TikTok pada Generasi Milenial, Gen Z, dan Alpha Menggunakan SVM dan Random Forest Rohmah, Nurbaiti; Aminudin, Nur; Wantoro, Agus; Ayu Andini, Dwi Yana
Jurnal Rekayasa Perangkat Lunak Vol. 4 No. 2 (2025): Jurnal Rekayasa Perangkat Lunak (J-Rapa)
Publisher : Universitas Aisyah Pringsewu

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

Abstract

Penelitian ini menganalisis dinamika kesehatan mental lintas generasi dalam komunitas K-Pop di TikTok Indonesia, dengan fokus pada FOMO, kecemasan, dan strategi coping digital. Pendekatan mixed-methods digunakan untuk mengintegrasikan survei terhadap 501 responden dan analisis 1.481 komentar publik. Survei mengukur empat konstruk psikologis utama, sementara komentar diklasifikasikan menggunakan algoritma Support Vector Machine (SVM) dan Random Forest, serta divalidasi secara manual melalui analisis tematik. Hasil menunjukkan bahwa Generasi Z memiliki tingkat FOMO dan kecemasan tertinggi, Milenial mengalami stres dan burnout, sedangkan Alpha menunjukkan keterlibatan digital yang pasif namun berisiko terhadap perkembangan sosial-emosional. Random Forest menunjukkan performa klasifikasi terbaik (F1-score 93%), unggul dalam menangkap ekspresi minoritas seperti trauma dan refleksi eksistensial.Temuan ini memperkuat bahwa TikTok bukan sekadar ruang hiburan, melainkan arena ekspresi psikologis yang kompleks. Penelitian ini berkontribusi pada pengembangan kerangka kerja kesejahteraan digital yang adaptif, dengan menekankan pentingnya validasi ganda dan intervensi berbasis data yang empatik.
KLASIFIKASI CHRONIC KIDNEY DISEASE (CKD) MENGGUNAKAN TOOLS WEKA, RAPIDMINER, DAN ORANGE DATA MINING: ANALISIS PERBANDINGAN KINERJA Wantoro, Agus
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8951

Abstract

Chronic Kidney Disease (CKD) merupakan salah satu penyakit tidak menular dengan tingkat prevalensi dan mortalitas yang terus meningkat secara global. Deteksi dini CKD sangat penting untuk mencegah komplikasi dan memperpanjang harapan hidup pasien. Penelitian ini bertujuan untuk membandingkan performa algoritma klasifikasi yang diterapkan pada dua platform data mining populer, yaitu WEKA, RapidMiner, dan Orange dalam menganalisis dataset penyakit ginjal kronis dari UCI Machine Learning (ML) Repository. Lima algoritma klasifikasi digunakan dalam eksperimen, yaitu Naive Bayes, Support Vector Machine (SVM), Random Forest, k-NN, dan Logistic Regression dengan skema validasi silang 10-fold. Kinerja model dievaluasi berdasarkan Confusion Matrix berupa nilai accuracy, precision, dan recall. Hasil menunjukkan bahwa terdapat perbedaan performa antar algoritma pada masing-masing tools. Pada tools WEKA, algoritma Random Forest menunjukkan performa terbaik dengan akurasi 99.81% dan algoritma k-NN menunjukkan performa terburuk. Pada tools RapidMiner, algoritma k-NN justru menampilkan nilai terbaik dengan nilai akurasi 99.5%, sedangkan Niave Bayes menyusul di bawahnya. Pada tools Orange algoritma SVM dan Random Forest memiliki performa terbaik dengan nilai 99.8% dan algoritma terburuk k-NN. Secara umum tools WEKA memiliki kinerja yang lebih baik, disusul Orange, dan RapidMiner. Namun, setiap platform memiliki keunggulan masing-masing. Ketiga tools memiliki potensi yang besar dalam pengembangan sistem pendukung keputusan berbasis ML untuk diagnosis CKD
Co-Authors ., Rusliyawati Adam Japal Admi Syarif Andini, Dwi Yana Ayu Aria Dadi Wibisono Aryani, Venty Aviv Fitria Yulia Ayu Andini, Dwi Yana Ayu Sangging, Putu Ristyaning Damayanti Daniel Prasetyo Tarigan Dedi Darwis Dikpride Despa Dwi Feriyanto Ega Budiman Erliyan Susanto Fadly, Muhtad Ferly Ardhy Fernando, Yusra Hadibrata, Exsa Hari Soetanto Ikna Awaliyani Jayawarsa, A.A. Ketut Kurnia Muludi Lutfy, Azza’zunda Choibar Mehta, Abhishek R Muhammad Zihad Prasetyo Nisa Berawi, Khairun Nur Aminudin Panji Bintoro Permata, Permata Permatan Priandika, Adhie Thyo Redy Susanto, Erliyan Rohmah, Nurbaiti Rusliyawati Sampurna Dadi Rizkiono Sanriomi Sintaro Saputra, Dani Setiawansyah Setiawansyah Susanto, Erliyan Redy Sutyarso Sutyarso Syazili Mustofa Tahta Herdian Andika Verdian, Arry Wahyu Caesarendra Wamiliana Wamiliana Waqas Arshad, Muhammad Warsito Warsito Yana Ayu, Dwi Yuri Rahmanto Zulkifli