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All Journal Jurnal Keteknikan Pertanian Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIV : International Journal on Informatics Visualization Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IJCIT (Indonesian Journal on Computer and Information Technology) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) International Journal of New Media Technology ILKOM Jurnal Ilmiah Jurnal ULTIMATICS Jurnal Ilmiah Sinus INTI Nusa Mandiri Paradigma Policy Brief Pertanian, Kelautan, dan Biosains Tropika The Indonesian Journal of Computer Science
Toto Haryanto
Departemen Ilmu Komputer Fakultas Matematika Dan Ilmu Pengetahuan Alam Institut Pertanian Bogor
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Social Sciences

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Prediksi Perubahan Iklim Untuk Pertumbuhan Tanaman Jeruk Keprok Menggunakan Naïve Bayes Ahmad Chusyairi; Toto Haryanto; Rachmad Nur Hayat
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 18, No 1 (2023): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v18i1.9352

Abstract

Iklim merupakan suatu pola data yang sangat berpengaruh pada pertumbuhan tanaman di masa mendatang, Tanaman jeruk keprok merupakan bahan baku untuk kebutuhan pangan masyarakat, sehingga diperlukan strategi untuk menjaga stabilitasi produksi melalui berbagai strategi terutama memaksimalkan data yang mudah di akses di internet. Iklim memiliki komponen yang dapat mempengaruhi pertumbuhan suatu tanaman diantaranya temperatur udara, kelembapan udara, lama penyinaran, dan curah hujan. Dalam rangka memaksimalkan penelitian ini, metode teorema bayes digunakan untuk dapat melakukan klasifikasi data didasarkan pada nilai probabilitas iklim baik atau buruk pada pertumbuhan tanaman, diharapkan naïve bayes dengan akurasi sebesar 85%, presisi sebesar 83%, recall sebesar 100%, dan F1 Score sebesar 90% dapat membantu upaya menjaga stabilitasi pertumbuhan tanaman dengan memanfaatkan data iklim yang tersedia.
Explorasi Pola Batik Baru dengan Deep Convolutional Algorithme Generative Adversarial Networks (DCGANs) Sahrial Ihsani Ishak; Toto Haryanto; Tri Widodo; Angga Bayu Santoso
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 18, No 1 (2023): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v18i1.9531

Abstract

Batik merupakan kesenian tradisional yang berasal dari Indonesia yang memadukan seni, budaya dan teknologi dalam membuatnya. Keanekaragaman motif batik di Indonesia diperoleh dari nilai- nilai simbol, budaya berdasarkan setiap daerah yang terkait erat dengan kehidupan masyarakat itu sendiri. Pengembangan dalam teknologi mendorong untuk membuat terobosan inovasi dalam memaksimalkan jenis – jenis batik dengan pola terbaru. Inovasi dengan machine learning yaitu Deep Convolutional Algorithme Generative Adversarial Networks (DC-GAN) merupakan bentuk terobosan inovasi pengembangan lanjutan Generative Adversarial Networks (GAN) dalam membuat pola – pola terbaru untuk batik. Pengembangan ini akan menggunakan sembilan jenis batik daerah dengan total data gambar sebanyak tiga ribu tiga ratus sembilan puluh tujuh dan dilakukan proses iterasi sebanyak lima ribu kali.
Robust Digital Watermarking pada Arsip Vital Mnggunakan Metode Hybrid SVD Dengan DWT Alita Wulan Dini; Shelvie Nidya Neyman; Toto Haryanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.5003

Abstract

The development of Internet technology affects the dissemination of data, especially in vital government archives. This research uses a hybrid singular value decomposition (SVD) and discrete wavelet transform (DWT) method, which aims to protect the copyright of vital archives. The stages of the insertion and extraction process are carried out to test the effect of the alpha value on the quality (imperceptibility) and robustness of the inserted image by measuring the Peak Signal-to-Noise Ratio (PSNR), verifying similarity by measuring the Normalized Cross-Correlation (NC) and Structural Similarity Index (SSIM). The results of research with ten vital archives and a watermark protection logo in JPEG format with a size of 512x512 pixels obtained a maximum PSNR with a value of α = 0.01 of 41.0567 dB, NC of 0.98904, and SSIM of 0.98023 in the Cibereum Land Certificate. So, it can be proven that this method produces vital archive watermarks that can be extracted and are robust to JPEG compression attacks of 75%, median filtering 3x3, Gaussian noise 0.01, speckle noise 0.01, and salt and pepper noise 0.01 but not resistant to rotation 80 and cropping attacks 2%.
MACHINE LEARNING FOR PREDICTING SPREAD OF COVID-19 IN INDONESIA Nur Hayati; Eri Mardiani; Fauziah Fauziah; Toto Haryanto; Viktor Vekky Ronald Repi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5174

Abstract

In previous research, we carried out an analysis using the FBProphet model to predict the COVID-19 outbreak in Indonesia. The application of the FBProphet model to time series data is considered quite good because it produces a MAPE of 22.60% with a linear distribution. Additionally, based on the pattern in the previous dataset and the total number of active cases currently stands at 2,606, in this research we tried to use the Linear Regression (LR) model as a comparison with the FBProphet model by using additional data from the same data source, KAWALCOVID19 website. Data collection started from March 2, 2020 to December 19, 2021. The aim of this research is the same as previous research, namely predicting the spread of COVID-19. The analysis process is carried out by preprocessing the data by validating missing data and validating the format of the data variables. Then carry out descriptive analysis and data visualization so that it can be seen that in this 657 data there is a fluctuates data that non-periodically from July to August 2021. Next, model analysis is carried out using FBProphet and LR and validating the results of each model. The research results are in the form of evaluation metrics where the LR model gets better RMSE, MAE and MAPE values compared to FBProphet, namely 292.91; 178, 81 and 12.79%.
Modeling Of Hyperparameter Tuned RNN-LSTM and Deep Learning For Garlic Price Forecasting In Indonesia Azhari, Irmawati Carolina; Haryanto, Toto
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024): Issues January 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i2.10714

Abstract

In the Indonesian garlic industry, the unpredictability of garlic prices poses a substantial challenge, impacting the sector's stability and growth. This research aims to address this issue by developing a highly accurate predictive model using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The study employs a dataset spanning 782 days, meticulously divided with 80% dedicated to training and 20% to testing. The model, equipped with 50 LSTM units, undergoes intensive training over 100 epochs, with a batch size of 5. Its effectiveness is evaluated using the Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), revealing exceptional predictive capabilities. The model achieves a low RMSE and MAPE in both training and testing phases, underscoring its accuracy and reliability in forecasting garlic prices. These results indicate not only the success of the RNN-LSTM model in capturing the complex patterns of price fluctuations but also highlight the potential of machine learning in enhancing time series analysis. This breakthrough offers significant implications for stakeholders in the garlic industry, providing a powerful tool for informed decision-making and strategic market planning, thereby contributing to the sector's sustainable development and stability
OPTIMIZATION OF POTATO LEAF DISEASE IDENTIFICATION WITH TRANSFER LEARNING APPROACH USING MOBILENETV1 ARCHITECTURE Brawijaya, Herlambang; Rahmawati, Eva; Haryanto, Toto
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.4718

Abstract

Diseases affecting potato leaves frequently lead to significant setbacks for farmers, reducing the overall harvest and the quality of the potatoes. Given the critical need for prompt disease detection, this research introduces the use of the MobileNet framework grounded in the Convolutional Neural Network (CNN) for adept detection of potato leaf ailments. During the research, potato leaf images undergo processing, and their distinct features are gleaned using CNN. Then, harnessing the MobileNet framework, these images undergo classification to ascertain the existence of diseases. The aspiration is that the formulated model can pinpoint diseases with notable precision, rapid feedback, and enhanced computational adeptness. Initial findings underscore the potential of this methodology in discerning potato leaf diseases, providing renewed optimism for farmers grappling with plant health issues. Experiments using the Transfer Learning approach showed good performance in classification and displayed a high accuracy rate of 99.2%.
ABILITY CONVOLUTIONAL FEATURE EXTRACTION FOR CHILI LEAF DISEASE USING SUPPORT VECTOR MACHINE CLASSIFICATION Saputra, Rizal Amegia; Haryanto, Toto; Wasyianti, Sri
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.4961

Abstract

Chili plants are among the most commonly used food ingredients in various dishes in Indonesia. Leaves on chili plants are often affected by disease; if the disease is not treated immediately, it can damage the plant and cause crop failure. Early detection of chili plant diseases is important to reduce the risk of crop failure. The development of technology and the application of machine-learning algorithms can automatically monitor chili plants using a computer system. Using this algorithm, the system analyzes and identifies diseases that a camera can observe and record. In this study, the proposed method for feature extraction uses a convolutional neural network (CNN) algorithm with transfer learning using VGG19. For classification using SVM for training data, accuracy generated 95%, precision 95%, recall 95%, and F1-Score 95%, and testing data accuracy generated 90%, precision 89%, recall 90%, and F1-Score 89%, proving that the convolutional process with architecture VGG19 and SVM algorithm is acceptable for classification. In future research, other architectures or extraction fusions can be used to maximize the results.
DEEP LEARNING FOR AUTOMATIC CLASSIFICATION OF AVOCADO FRUIT MATURITY Widiati, Wina; Haryanto, Toto
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.5043

Abstract

Avocado (Persea Americana), a fleshy fruit with a single seed, has increased in popularity globally, especially in tropical and Mediterranean climates, thanks to its commercial and nutritional value. Rich in bioactive compounds, avocados contribute to the prevention and treatment of various diseases, including cardiovascular problems and cancer. Avocado production in Indonesia, for example, is showing a significant increase, reflecting the growing demand. Avocado ripeness affects shelf life and quality, making the determination of ripeness level a critical aspect of postharvest management. Skin color and pulp firmness change during storage, affecting quality and nutritional value. Proper classification of ripeness is important to reduce post-harvest losses, improve quality and optimize export costs. Recent research shows the use of technologies such as machine learning and YOLO (You Only Look Once) version 9 in real-time detection of avocado ripeness, offering innovative solutions to reduce post-harvest losses and improve distribution efficiency. This approach not only benefits farmers and consumers but also ensures consumer satisfaction and reduces economic losses. This study highlights the importance of real-time detection in monitoring avocado ripeness, where the training process was conducted for 89,280 iterations resulting in a new model for avocado ripeness detection. The final model has a mean Average Precision (mAP) validation value of 84.3%, mAP 84.3% signifies the optimal level of accuracy in object recognition in avocado fruit maturity images using the YOLO model that has undergone an intensive training process.
SINTESA CITRA DAUN KOPI MENGGUNAKAN GENERATIVE ADVERSARIAL NETWORK PADA DATASET PENYAKIT DAUN KOPI Wildah, Siti Khotimatul; Latif, Abdul; Haryanto, Toto
INTI Nusa Mandiri Vol. 19 No. 1 (2024): INTI Periode Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i1.5045

Abstract

Coffee, as the second most traded commodity after petroleum, faces production challenges, especially due to pest or disease attacks on coffee leaves. Therefore, it is important to carry out early detection of the disease in order to minimize the risk and apply special treatment. Automatic detection of disease can be done through the application of Computer Vision technology. However, one of the main challenges faced is the limited training dataset. Generative Adversarial Networks (GANs) is a Deep Learning method that is capable of modifying images with high quality. This research aims to synthesize coffee leaf images based on the public Coffee Leaf Disease dataset using the GANs method. Testing was carried out using the RMSProp optimizer, the learning rate was 0.0001 and was carried out for 300 epochs. The architecture built uses 26 layers in the generator model and 15 layers in the discriminator model. The results of the test show that the drilled network obtained an MMSE value of 0.1658, which is not too high because the resulting synthesized image is not very good.
The Development of Classification Algorithm Models on Spam SMS Using Feature Selection and SMOTE Chrysanti, Rachma; Wijaya, Sony Hartono; Haryanto, Toto
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2220.356-370

Abstract

Short Message Service (SMS) is a widely used communication media. Unfortunately, the increasing usage of SMS has resulted in the emergence of SMS spam, which often disturbs the comfort of cellphone users. Developing a classification model as a solution for filtering SMS spam is very important to minimize disruption and loss to cellphone users due to SMS spam. To address this issue, utilize the Naïve Bayes algorithm and Support Vector Machine (SVM) along with Chi-square and Information Gain. This study focuses on the classification and analysis of SMS spam on a cellular operator service in a telecommunications company using machine learning techniques. This study applies and combines a combination of classification methods including Naive Bayes and Support Vector Machine (SVM). The combination is carried out with Chi-square and Information Gain feature selection to reduce irrelevant features. This study also applies a combination with data balancing techniques using the Synthetic Minority Oversampling Technique (SMOTE) to balance the number of unbalanced classes. The results show that SMOTE improves classification performance. SVM performs spam SMS classification or not spam Model 7 (SVM) achieves accuracy 98,55% and it has improved the performance when it was combined with SMOTE Model 10 (SVM + SMOTE) achieves F1-score 99,23% in performing spam SMS classification or not this outperforms all other models. These results indicate that the SVM algorithm achieved better performance in detecting spam SMS compared to Naive Bayes, which demonstrated a lower level of accuracy. These results illustrate the effectiveness of combining machine learning models to enhance classification accuracy with balanced data, emphasizing the model that exhibited the most substantial improvement in performance.
Co-Authors Abdul Latif Aditika, Exciyona Adline Twince Tobing, Fenina Agus Buono Alita Wulan Dini Angga Bayu Santoso Aniati Murni Annisa Annisa Anuraga Jayanegara Arham, Iffah Luthfiyah Asterika Prawesti Azhari, Irmawati Carolina Aziz Kustiyo Budiman Budiman Chrysanti, Rachma Chusyairi, Ahmad Cian Ramadhona Hassolthine Dasri, Dasri Djuara P Lubis Dwi Retno Hapsari, Dwi Retno Endang Purnama Giri Eri Mardiani Eva Rahmawati Eylen Desy Novita Fauziah Fauziah Ganda Wijaya Guritno, Hanif Bagus Hari Agung Adrianto Hartono Wijaya, Sony Hehanussa, Siti Gayatri Herlambang Brawijaya Heru Suhartanto Imam Nawawi Imas Sukaesih Sitanggang Irman Hermadi Irzal Effendi Jamaludin JAMALUDIN Jasni Mohammad Zain Khairani Kusmardi Kusmardi Lidya Ningsih Mahmudi Siwi, Mahmudi Marimin , Muhammad Ikhwani Saputra Muji Yuswanto Mushthofa Nababan, Adi Pandu Rahmat Niken TM Pratiwi Niken TM Pratiwi Noer Muslimah, Meia Nur Hayati Rachmad Nur Hayat Ridwansyah Ridwansyah Rizal Amegia Saputra Rizky Kurniawan RR. Ella Evrita Hestiandari Sahrial Ihsani Ishak Shelvie Nidya Neyman Silvia Rahmi Silvia Rahmi Siti Khotimatul Wildah Sony Hartono Wijaya Sony Muhammad Sri Wahjuni Titania Aulia Tobing, Fenina Adline Twince Tri Widodo Verry Riyanto Viktor Vekky Ronald Repi Wasyianti, Sri Wina Widiati Yusoff, Marina