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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Edutech Semantik Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Ilmiah Kursor Jurnal Transformatika International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Tech-E Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CogITo Smart Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Data Science: Journal of Computing and Applied Informatics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JOURNAL SCIENTIFIC OF MANDALIKA (JSM) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Teknologi Informasi Cyberku Studies in English Language and Education Moneter : Jurnal Keuangan dan Perbankan Scientific Journal of Informatics Journal on Pustaka Cendekia Informatika
Pulung Nurtantio Andono
Universitas Dian Nuswantoro
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Analisis Keamanan Steganografi Multi-Layer dengan Enkripsi Vigenère dan Caesar Cipher pada Citra Digital hidayat, Sholeh; Nurtantio Andono, Pulung
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 3 (2025): JPTI - Maret 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.786

Abstract

Kemajuan teknologi komunikasi data menghadirkan tantangan baru terkait manipulasi dan kebocoran informasi, sehingga diperlukan metode pengamanan yang lebih efektif dan andal. Kriptografi dapat mengenkripsi pesan agar sulit dipecahkan, tetapi keberadaannya masih dapat dikenali dan berisiko dianalisis lebih lanjut. Untuk mengatasi kelemahan ini, penelitian ini mengombinasikan steganografi Least Significant Bit (LSB) dengan enkripsi multi-layer menggunakan Vigenère Cipher dan Caesar Cipher guna meningkatkan keamanan data digital. Metode ini menerapkan dua lapisan perlindungan : pertama, enkripsi ganda yang mengacak pesan sebelum penyisipan agar lebih sulit direkonstruksi; kedua, teknik steganografi LSB yang menyisipkan pesan terenkripsi ke dalam citra digital tanpa mengubah struktur visual secara mencolok. Eksperimen dilakukan dengan mengukur kualitas citra hasil steganografi menggunakan PSNR dan analisis histogram untuk menilai perubahan visual. Hasil penelitian menunjukkan bahwa metode ini mampu menyisipkan pesan secara optimal tanpa menurunkan kualitas citra secara signifikan, dengan PSNR tetap berada pada tingkat tinggi, yang menunjukkan bahwa perbedaan antara citra stego dan citra asli hampir tidak terlihat. Selain itu, analisis histogram membuktikan bahwa distribusi piksel sebelum dan sesudah penyisipan tetap stabil, sehingga metode ini sulit dideteksi oleh analisis visual. Dengan demikian, pendekatan kombinasi kriptografi dan steganografi ini terbukti efektif dalam meningkatkan keamanan data digital tanpa mengorbankan kualitas visual, sehingga dapat digunakan sebagai solusi andal untuk perlindungan informasi dalam komunikasi modern.
Improving Random Forest Performance for Sentiment Analysis on Unbalanced Data Using SMOTE and BoW Integration: PLN Mobile Application Case Study Rahmatullah, Muhammad Rifqi Fadhlan; Andono, Pulung Nurtantio; Affandy; Soeleman, M. Arief
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.19295

Abstract

Purpose: This research aims to improve the accuracy of sentiment analysis on PLN Mobile app reviews by overcoming the challenge of data imbalance. This goal is important to provide a better understanding of user opinions and support PT PLN (Persero) in improving mobile application services. Methods: This research uses the Random Forest algorithm combined with Synthetic Minority Over-sampling Technique (SMOTE) to handle imbalanced data. Data is collected through web scraping reviews from the Google Play Store, followed by preprocessing processes such as data cleaning, stopword removal, tokenization, and stemming. Feature extraction is performed using the Bag of Words (BoW) method, and the data is tested with four sharing schemes. Result: The results showed that the 90%-10% sharing scheme gave the best performance with an accuracy of 81% and an average precision and recall of 0.79. This finding confirms that the larger the proportion of training data, the better the model performs sentiment classification. Novelty: This research's novelty lies in combining SMOTE with BoW and Random Forest to overcome data imbalance. This approach is a significant reference for future sentiment analysis research. It provides practical insights that PT PLN (Persero) can use to improve the quality of its application services.
Model Neural Network untuk Memprediksi Tingkat Kemenangan Berdasarkan Draft Pick Mobile Legends Hamir, Mun; Nurtantio Andono, Pulung
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 4 (2025): JPTI - April 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.723

Abstract

 Banyak tim dalam permainan Mobile Legends menghadapi tantangan dalam menentukan kombinasi hero yang optimal selama fase draft pick, sehingga strategi pemilihan hero yang kurang tepat sering kali menjadi penyebab utama kekalahan. Penelitian ini bertujuan untuk memprediksi tingkat kemenangan dalam permainan Mobile Legends berdasarkan draft pick yang dipilih oleh masing-masing tim dengan memanfaatkan model Neural Network. Data penelitian dikumpulkan dari 500 pertandingan profesional dan kasual dengan fitur yang mencakup draft pick, peran hero, sinergi tim, counter-pick, dan hasil pertandingan. Model Neural Network dibangun dengan tiga hidden layer (128-64-32 neuron) menggunakan fungsi aktivasi ReLU dan binary cross-entropy loss, serta dilatih menggunakan TensorFlow dan Keras untuk mencapai performa prediksi yang optimal. Hasil penelitian menunjukkan bahwa model mampu mencapai akurasi prediksi sebesar 85%, dengan kombinasi hero yang memiliki sinergi kuat dan strategi counter-pick efektif dapat meningkatkan peluang kemenangan hingga 20% dibandingkan tim yang tidak memperhatikan aspek tersebut. Penelitian ini diharapkan dapat memberikan kontribusi penting dalam pengembangan strategi drafting yang optimal di dunia esports, membantu tim dalam pengambilan keputusan selama fase draft pick, serta meningkatkan peluang kemenangan secara signifikan dalam permainan Mobile Legends.
Klasifikasi SVM Menggunakan Optimasi PSO Untuk Kelayakan Biji Kopi Dengan Level Medium Roast Saputro, Wicaksono Agung; Andono, Pulung Nurtantio; Soeleman, M Arief
Techno.Com Vol. 24 No. 2 (2025): Mei 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i2.12657

Abstract

Biji kopi dengan medium roast memiliki ciri warna coklat muda kehitaman, permukaan sedikit berminyak, dan retakan biji yang tidak terlalu lebar. Karena kopi merupakan hasil bumi yang banyak dikonsumsi dengan tingkat kematangan medium, diperlukan quality control yang efektif untuk memastikan biji kopi layak konsumsi. Penelitian ini mengambil sampel dari perusahaan terkait dan menggunakan metode GLCM untuk ekstraksi fitur numerik dari biji kopi, serta SVM dengan kernel RBF untuk klasifikasi, mengingat pentingnya pemilihan kernel dan parameter dalam menentukan akurasi. Untuk meningkatkan akurasi, diterapkan optimasi menggunakan PSO. Hasil klasifikasi menggunakan SVM saja mencapai akurasi 85,37%, sedangkan dengan optimasi PSO, akurasi meningkat menjadi 93,57%, menunjukkan bahwa penerapan PSO pada algoritma SVM mampu meningkatkan performa klasifikasi biji kopi medium roast secara signifikan. Kata kunci: SVM, PSO, Biji Kopi Medium Roast
Latent Semantic Analysis (LSA) Dengan Metode Support Vector Machine (SVM) dan Algoritma Naïve Bayes Pada Identifikasi Berita Palsu Dito, Aliffia Putri; Pulung Nurtantio Andono; M. Arief Soeleman
Journal Scientific of Mandalika (JSM) e-ISSN 2745-5955 | p-ISSN 2809-0543 Vol. 6 No. 10 (2025)
Publisher : Institut Penelitian dan Pengembangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/10.36312/vol6iss10pp3837-3850

Abstract

Berita palsu atau disebut hoax banyak beredar di masyarakat. Penyebaran berita palsu dapat mudah menyerap masyarakat terlebih melalui media sosial. informasi yang tersebar melalui platform media sosial sangat mudah terserap bagi masyarakat. Para pengguna media sosial biasanya menjadi pembuat konten dengan jumlah penyebaran informasi yang cukup luas, dan memungkinkan adanya misinformasi yang tidak dapat diabaikan. Kredibilitas dari sumber informasi tersebut juga sangat penting untuk menghindari resiko mengkonsumsi berita palsu. Menurut data statistik yang diterbitkan oleh Stanford University academics, sebanyak 72,3 persen berita palsu berasal dari outlet berita sosial dan platform media sosial online. Identifikasi dalam berita palsu tersebut semakin meningkat penggunaannya namun pemeriksaan fakta dalam banyak kasus cukup sulit, memakan waktu dan memerlukan biaya yang besar. Penelitian ini dilakukan dengan menggunakan latent semantic analysis dengan metode support vector machine dan algoritma naïve bayes dalam identifikasi berita palsu. Hasil pengujian ini menghasilkan nilai akurasi sebesar 82,28% dengan metode support vector machine dan 81,39% pada algoritma naïve bayes.
Optimasi Hyperparameter Convolutional Neural Network untuk Klasifikasi Jenis Penyakit Daun Jagung menggunakan CLAHE Al-Fatih, Gilang Fajar; Pulung Nurtantio Andono; M. Arief Soeleman
Tech-E Vol. 9 No. 1 (2025): Tech-E Journal Vol. 9 No. 1 features 6 articles contributed by 21 authors from
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v9i1.3541

Abstract

Corn plays an important role as one of the main food sources in Indonesia and around the world. Diseases in corn plants are often visible through their leaves. However, problems arise when farmers have difficulty detecting diseases that attack corn plants, making it difficult to take appropriate action to control them. Diseases in corn plants can lead to reduced photosynthesis, disrupt agricultural productivity, and cause financial losses for farmers. Therefore, a digital approach that can detect various types of diseases in corn plants is highly needed. In recent years, the emergence of machine learning algorithms has provided support systems for classifying corn leaf diseases. This research aims to classify types of corn leaf diseases using the Optimization of Convolutional Neural Network (CNN) Method for Classifying Types of Corn Leaf Diseases Using Contrast Limited Adaptive Histogram Equalization (CLAHE). The research stages include data collection, image enhancement with CLAHE, data augmentation, data preprocessing, classification, and evaluation. The Optimization of the CNN Method for Classifying Types of Corn Leaf Diseases Using CLAHE resulted in an accuracy of 94%, indicating that this experiment is capable of classifying corn leaf diseases effectively.
DESIGN OF IOT AND ONION AGRICULTURE DATABASE USING BPR LIFE CYCLE Thifaal, Nisrina Salwa; Alzami, Farrikh; Steven, Alvin; Yusianto, Rindra; Saputra, Filmada Ocky; Sartika, Mila; Andono, Pulung Nurtantio; Wahyudi, Firman
Moneter: Jurnal Keuangan dan Perbankan Vol. 11 No. 1 (2023): APRIL
Publisher : Universitas Ibn Khladun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (632.824 KB) | DOI: 10.32832/moneter.v11i1.54

Abstract

One of the food commodities produced by the agricultural sector with high economic value is red onion. As the population of Indonesia increases, the need for red oniom has also increased. The level of red onion production from year to year is also increasing. Especially the central Java area as the largest red onion producing center in 2021. Therefore, the amount of red onion production needs to be maintained and increased by monitoring overall land conditions. Such as weather conditions, air, temperature, and humidity. A sensor to detect these factors is already available but there is no database to accommodate the data from the sensor. The purpose of this research is to produce a Business Process Model and Notation (BPMN) of red onion surveillance system on Internet of Things (IoT) based farmland. The stages carried out are by collecting data related to the research and analyzing business processes using the Business Process Reengineering Life Cycle (BPR) method. This method improves business processes to become more efficient and renewable. This research produces a database design to accommodate incoming data from Internet of Things sensors. Things (IoT) on red onion farming.
Implementation Of Extreme Gradient Boosting Algorithm For Predicting The Red Onion Prices Saputri, Pungky Nabella; Alzami, Farrikh; Saputra, Filmada Ocky; Andono, Pulung Nurtantio; Megantara, Rama Aria; Handoko, L Budi; Umam, Chaerul; Wahyudi, Firman
Moneter: Jurnal Keuangan dan Perbankan Vol. 11 No. 1 (2023): APRIL
Publisher : Universitas Ibn Khladun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (656.456 KB) | DOI: 10.32832/moneter.v11i1.55

Abstract

Red Onion or the Latin name Allium Cepa is included in the group of vegetable plants that are needed by the public for food needs. Red Onions are one of the seasonal crops so their availability can change in the market which causes price instability due to a lack of supply of production by several factors: 1) not yet it's harvest time, 2) crop attacked disease pests and fungi, and 3) weather factor. Therefore, a study is needed to predict red onion prices, so that it can be used as information for the government to stabilize red onion prices. The method used in this study is CRISP-DM and the Extreme Gradient Boosting algorithm to predict the price of red onions by taking data samples from Tegal and Pati Cities. The results of this study are that the Extreme Gradient Boosting algorithm is able to produce Tegal District Root Mean Square Error (RMSE) values of 5107.97% and Mean Absolute Percentage Error (MAPE) values of 0.17%. For prediction results with Pati Regency data samples, it produces a Root Mean Square Error (RMSE) value of 6049.74% and a Mean Absolute Percentage Error (MAPE) of 0.17%.
Deep Learning-Based Eye Disorder Classification: A K-Fold Evaluation of EfficientNetB and VGG16 Models Paramita, Cinantya; Rakasiwi, Sindhu; Andono, Pulung Nurtantio; Shidik, Guruh Fajar; Shier Nee Saw; Rafsanjani, Muhammad Ivan
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.26257

Abstract

Purpose: The study evaluates EfficientNetB3 and VGG16 deep learning architectures for image classification, focusing on stability, accuracy, and interpretability. It uses Gradient-weighted Class Activation Mapping to improve transparency and robustness. The research aims to create reliable AI-based diagnostic tools. Methods: The study used a dataset of 4,217 color retinal fundus images divided into four classes: cataract, diabetic retinopathy, glaucoma, and normal. The dataset was divided into 70% for training, 10% for validation, and 20% for testing. The researchers used a transfer learning approach with EfficientNetB3 and VGG16 models, pretrained on ImageNet. Real-time augmentation was applied to prevent overfitting and improve generalization. The models were compiled with the Adam optimizer and trained with categorical cross-entropy loss. Early stopping was implemented to allocate computational resources efficiently and reduce overfitting. A learning rate scheduler (ReduceLROnPlateau) was added to adjust the learning rate if no significant improvement was made concerning validation loss. EfficientNetB3 was more efficient in model size, possessing only 12 million parameters compared to VGG16's 138 million, making it suitable for resource-constrained mobile or embedded systems. The final evaluation was done on the held-out test set. Result: The EfficientNetB3 architecture outperforms VGG16 in classification accuracy and loss value stability, with an average accuracy of 93%. It also exhibits better transparency and predicted accuracy, making it a reliable model for medical image categorization. Novelty: This work introduces a novel framework integrating EfficientNetB3 architecture, stratified cross-valuation, L2 regularization, and Grad-CAM-based interpretability, focusing on openness and explainability in model evaluation.
Improved Chaotic Image Encryption on Grayscale Colorspace Using Elliptic Curves and 3D Lorenz System Sinaga, Daurat; Jatmoko, Cahaya; Astuti, Erna Zuni; Rachmawanto, Eko Hari; Abdussalam, Abdussalam; Pramudya, Elkaf Rahmawan; Shidik, Guruh Fajar; Andono, Pulung Nurtantio; Doheir, Mohamed
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2251

Abstract

Digital data, especially visual content, faces significant security challenges due to its susceptibility to eavesdropping, manipulation, and theft in the modern digital landscape. One effective solution to address these issues is the use of encryption techniques, such as image encryption algorithms, that ensure the confidentiality, integrity, and authenticity of digital visual content. This study addresses these concerns by introducing an advanced image encryption method that combines Elliptic Curve Cryptography (ECC) with the 3D Lorenz chaotic system to enhance both security and efficiency. The method employs pixel permutation, ECC-based encryption, and diffusion using pseudo-random numbers generated by the Lorenz 3D system. The results show superior performance, with an MSE of 3032 and a PSNR of 8.87 dB, as well as UACI and NPCR values of 33.34% and 99.64%, respectively, indicating strong resilience to pixel intensity changes. During testing, the approach demonstrated robustness, allowing only the correct key to decrypt images accurately, while incorrect or modified keys led to distorted outputs, ensuring encryption reliability. Future work could explore extending the method to color images, optimizing processing for larger datasets, and incorporating additional chaotic systems to further fortify encryption strength.
Co-Authors Abdussalam Abdussalam, Abdussalam Achmad Ridwan Affandy Agus Winarno, Agus Al zami, Farrikh Al-Fatih, Gilang Fajar Alzami, Farrikh Aria Hendrawan, Aria Arry Maulana Syarif, Arry Maulana Asih Rohmani Asih Rohmani, Asih Bastiaans, Jessica Carmelita Budi Harjo Cahaya Jatmoko Candhy Fadhila Arsyad Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto Chaerul Umam Christy Atika Sari D, Ishak Bintang Dalimarta, Fahmy Ferdian Danang Bagus Chandra Prasetiyo Darmawan, Aditya Aqil Denny Senata Dito, Aliffia Putri Doheir, Mohamed Dwi Eko Waluyo Dwi Puji Prabowo, Dwi Puji Dwiza Riana Edi Noersasongko Edi Noersasongko Edi Noersasongko Egia Rosi Subhiyakto, Egia Rosi Ekaprana Wijaya Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erna Zuni Astuti Fajrian Nur Adnan Fauzi Adi Rafrastara Firman Wahyudi, Firman Fitri Yakub Guruh Fajar Shidik Hamir, Mun Hanny Haryanto Hartojo, James Harun Al Azies Heru Lestiawan Hidayat, Sholeh Hisyam Syarif Husain Husain I Ketut Eddy Purnama Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Irwan, Rhedy Islam, Hussain Md Mehedul Ivan Maulana Jumanto Jumanto, Jumanto Junta Zeniarja Karis Widyatmoko Khafiizh Hastuti Kiat, Ng Poh Kunio Kondo L. Budi Handoko M Arief Soeleman M. Arief Soeleman M. Arif Soeleman Maria Goretti Catur Yuantari Megantara, Rama Aria Mila Sartika, Mila Minghat, Asnul Dahar Bin Moch Arief Soeleman Moch Arief Soeleman Moch Arief Soeleman, Moch Arief Mochamad Hariadi Mochammad Arief Soeleman Muhammad Munsarif Muhammad Naufal, Muhammad Muljono Muljono Nanna Suryana Herman Ningrum, Novita Kurnia Nita Merlina Noor Ageng Setiyanto, Noor Ageng Nur Azise Ocky Saputra, Filmada Panca Hutama Caniago Paramita, Cinantya Pergiwati, Dewi Pramitasari, Ratih Prasetyoningrum, Devi Puji Purwatiningsih, Aris Pujiono Pujiono Purwanto Purwanto Putra, Angga Permana Raden Arief Nugroho Rafsanjani, Muhammad Ivan Rahmatullah, Muhammad Rifqi Fadhlan Ramadhan Rakhmat Sani ramayanti, ismarita Ricardus Anggi P Ricardus Anggi Pramunendar Rohman, Muhammad Syaifur Ruri Suko Basuki Saputra, Filmada Ocky Saputri, Pungky Nabella Saputro, Wicaksono Agung Saraswati, Galuh Wilujeng Sari Ayu Wulandari Sarker, Md. Kamruzzaman Satriyawibawa, Muhammad Yiko Savicevic, Anamarija Jurcev Senata, Denny Sendi Novianto Shafa, Raihanaldy Ash Shier Nee Saw Sinaga, Daurat Sindhu Rakasiwi Siti Hadiati Nugraini Soeleman, M Arief Soeleman, M. Arief Soeleman, Moch. Arief Soong, Lim Way Sri Winarno Sri Winarno Steven, Alvin Sudibyo, Usman Sukmawati Anggraeni Putri, Sukmawati Anggraeni Sukmono, Indriyo K. Supriyono Asfawi Susanto Susanto Tendi Tri Wiyanto, Tendi Tri Tengku Riza Zarzani N Thifaal, Nisrina Salwa Torhino, Rizal Wellia Shinta Sari Yaacob, Noorayisahbe Mohd Yusianto Rindra Zahrotul Umami, Zahrotul Zainal Arifin Hasibuan