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All Journal Foristek Semantik International Journal of Advances in Intelligent Informatics Jurnal Sistem dan Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL ILMIAH MATEMATIKA DAN TERAPAN JOURNAL OF APPLIED INFORMATICS AND COMPUTING Technomedia Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Indonesian Journal of Electrical Engineering and Computer Science IAIC Transactions on Sustainable Digital Innovation (ITSDI) Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Abmas Negeri (JAGRI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Algoritma The Indonesian Journal of Computer Science Riau Jurnal Teknik Informatika Jurnal Informatika dan Kesehatan Bomba: Jurnal Pembangunan Daerah
Mohammad Yazdi Pusadan
Program Studi Teknik Informatika, Universitas Tadulako, Palu, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Public Health Social Sciences Transportation Veterinary Other

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The Image Extraction Using the HSV Method to Determine the Maturity Level of Palm Oil Fruit with the k-nearest Neighbor Algorithm Mohammad Yazdi Pusadan; Indah Safitri; Wirdayanti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5558

Abstract

The oil palm is one of the monocot oil-producing plants in Indonesia. Sorting errors in oil palm fruit are caused by a sorter error when distinguishing the color of ripe and immature oil palm fruit. In addition to inefficient time, the area of oil palm plantations is also a factor that causes the sorter to make mistakes in sorting. This study aims to produce a system that can classify the maturity of oil palms based on feature extraction of characteristics of the hue, saturation and value (HSV) color features. The HSV method is used to produce color characteristics from the image of the oil palm fruit. Classification of oil palm fruit maturity is classified using the K-Nearest Neighbor (KNN) algorithm with a dataset of 400 oil palm fruit image data with a data sharing ratio of 70% training data and 30% test data. 280 image data were used as training data, divided into 140 image data of ripe oil palm fruit, 140 image data of immature oil palm fruit and 120 image data of oil palm used as test data which is divided into 60 image data of ripe oil palm and 45 unripe palm oil. Based on the result of tests that have been carried out using a confusion matrix with varied k values, namely, 5 and 7, the average precision is 94.16%.
Utilization of EfficientNet-B0 to Identify Oncomelania Hupensis Lindoensis as a Schistosomiasis Host Lamadjido, Moh. Raihan Dirga Putra; Laila, Rahmah; Pusadan, Mohammad Yazdi; Yudhaswana, Yuri; Lapatta, Nouval Trezandy; Ngemba, Hajra Rasmita
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9058

Abstract

Schistosomiasis caused by the Schistosoma japonicum worm is a significant health problem in Indonesia, especially in endemic areas such as the Napu Plateau and Bada Plateau. The main problem in controlling this disease is the difficulty in rapid and accurate identification of Oncomelania hupensis lindoensis snails as intermediate hosts of the parasite. This research aims to develop an artificial intelligence-based system that can efficiently identify the snail species. The stages of this research include collecting snail image data from the Central Sulawesi Provincial Health Office, consisting of 2100 images covering seven snail species, then processed through preprocessing and augmentation stages. The model applied was EfficientNet-B0. The results showed that the EfficientNet-B0 model achieved 98.80% training accuracy and 98.33% validation accuracy. Confusion matrix testing showed good performance, with an accuracy of 98% and for the species Oncomelania hupensis lindoensis had a recall of 93%, precision of 100%, F1-score of 97%, and the resulting AUC value of 99.7%. This research successfully developed an efficient identification system, which is expected to help health surveillance personnel in accelerating the identification process of schistosomiasis intermediate hosts.
Digitalisasi Pembelajaran Budaya Sulawesi Tengah melalui Augmented Reality Menggunakan Metode Marker-Based Tracking Saleh, Muhammad Taufik; Lamasitudju, Chairunnisa Ar.; Pusadan, Yazdi; Laila, Rahmah; Pratama, Septiano Anggun
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4195

Abstract

Central Sulawesi has a diverse cultural heritage, but the rapid development of technology poses new challenges in maintaining the interest of the younger generation in local culture. This research developed a culture-learning application based on Augmented Reality (AR) using the Marker Based Tracking method for students at SDN Inpres 2 Tanamodindi to address these challenges. The application, "Mari Berbudaya," is designed to increase students' interest in local culture by providing an interactive and innovative learning experience through AR technology. This study employs a qualitative approach and prototyping method. Black box testing results confirm that all main functions of the application work well, while distance testing shows that markers can be optimally detected up to a distance of 1 meter. A questionnaire evaluation of the students resulted in an overall score of 89% with a classification of very feasible. Thus, from the overall evaluation, the "Mari Berbudaya" application has proven effective in increasing students' interest and understanding of Central Sulawesi's culture through AR technology.
The Implementation and Analysis of The Proof of Work Consensus in Blockchain Therry, Alvin Christian Davidson; Ardiansyah, Rizka; Pusadan, Mohammad Yazdi; Joefrie, Yuri Yudhaswana; Kasim, Anita Ahmad
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17878

Abstract

Communication in peer-to-peer (P2P) networks presents challenges in maintaining security, data integrity, and decentralization. Consensus mechanisms play a crucial role in addressing these challenges by validating data and ensuring that each entity has synchronized data without intermediaries. This research focuses on the implementation and analysis of the Proof of Work (PoW) consensus mechanism, widely used in blockchain, to enhance understanding of its functions, benefits, and workings or flow. This research, conducted using the Go programming language, successfully implements Proof of Work (PoW) as a security measure, ensuring data integrity, and preventing manipulation. Through black-box testing, this research confirms the functionality and reliability of the implemented Proof of Work (PoW) consensus. These findings contribute to a deeper understanding of consensus mechanisms, offering insights to optimize blockchain protocols and foster trust among entities. This research highlights the relevance of sustainable Proof of Work (PoW) in blockchain technology, emphasizing its role in enhancing security and ensuring data integrity in decentralized networks.
Implementation of Data Layer In Blockchain Network Using SHA256 Hashing Algorithm Sondakh, Clivent Gerhard; Ardiansyah, Rizka; Joefrie, Yuri Yudhaswana; Angreni, Dwi Shinta; Pusadan, Mohammad Yazdi
Advance Sustainable Science, Engineering and Technology Vol 6, No 2 (2024): February - April
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i2.18103

Abstract

The escalating demand for secure data management in blockchain systems has prompted the exploration of advanced cryptographic techniques. Leveraging the SHA256 hashing algorithm, this implementation aims to fortify data integrity, confidentiality, and authentication within the blockchain network. By meticulously examining the algorithm's application, the research demonstrates its efficacy in ensuring tamper-resistant data storage and retrieval, quantifying improvements in security percentages and specific metrics. The integration of SHA256 within the data layer is explored in technical detail, highlighting the concrete benefits of heightened security and immutability. The analysis discusses practical implications and delves into potential advancements in blockchain technology, offering valuable insights for researchers, developers, and practitioners seeking to bolster the robustness of data layers in blockchain networks.
Developing Decentralized Data Storage Network Using Blockchain Technology to Prevent Data Alteration Putra, Ryan Adi; Ardiansyah, Rizka; Pusadan, Mohammad Yazdi; Kasim, Anita Ahmad; Joefrie, Yuri Yudhaswana
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17772

Abstract

In the face of escalating global data exchange, the pronounced vulnerability oftraditional centralized storage networks to manipulation and attacks poses a pressing challenge. Digital service providers, entrusted with vast datasets, grapple with the formidable task of ensuring the security, integrity, and continuous availability of their stored information. This paper tackles these multifaceted issues by proposing a decentralized data storage network empowered by blockchain technology. This approach systematically mitigates the inherent susceptibilities of centralized systems, thereby providing heightened resilience against unauthorized alterations and malicious attacks that compromise digital information integrity. Moreover, the decentralized model holds significant promise for securing public data. By leveraging the transparency and immutability of blockchain ledgers, this approach not only safeguards against unauthorized access but also actively fosters transparency and accountability in data management. This makes it particularly well-suited for ensuring the security and integrity of public data, addressing concerns related to trust and reliability in the ever-evolving landscape of information exchange.
Evaluasi Performa Proof of Work dan Proof of Stake melalui Uji Stres Beban Tinggi Blockchain Yulianti, Indira; Ardiansyah, Rizka; Yazdi Pusadan, Mohammad; Amriana; Lamasitudju, Chairunnisa
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2500

Abstract

Consensus mechanisms play a crucial role in determining the efficiency and scalability of blockchain systems. The two most commonly used algorithms are Proof of Work and Proof of Stake, each exhibiting distinct performance characteristics under high transaction loads. This study aims to evaluate and compare the performance of both consensus mechanisms through a simulation-based experimental approach. Testing was conducted using the Hardhat framework in a local environment under two primary scenarios: transaction scaling and burst transaction.Four evaluation metrics were employed: throughput, transaction latency, finality time, and mempool congestion. The results indicate that Proof of Stake consistently outperforms across all four metrics, demonstrating high throughput, stable latency and finality time, and controlled mempool congestion. In contrast, Proof of Work shows a significant decline in performance under heavy load due to its static and non-adaptive mining process.The Mann-Whitney U statistical test confirms that the performance differences are statistically significant across nearly all metrics. This research provides deeper insights into the strengths and limitations of each consensus mechanism under high-load conditions using Hardhat, and contributes to a broader understanding of blockchain scalability in real-world applications. The findings suggest that Proof of Stake is more suitable for large-scale blockchain implementations that demand high efficiency and speed.
Pattern recognition for facial expression detection using convolution neural networks Pusadan, Mohammad Yazdi; Sasuwuk, James Rio; Pratama, Septiano Anggun; Laila, Rahma
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1602

Abstract

The COVID-19 pandemic was a devastating disaster for humanity worldwide. All aspects of life were disrupted, including daily activities and education. The education sector faced significant challenges at all levels, from kindergarten to elementary, junior high, and high school, as well as in higher education, where learning had to be online. Human emotions are primarily conveyed through facial expressions resulting from facial muscle movements. Facial expressions serve as a form of nonverbal communication, reflecting a person’s thoughts and emotions. This research aims to classify emotions based on facial expressions using the Convolutional Neural Network (CNN) and detect faces using the Viola-Jones method in video recordings of online meetings. We utilize the VGG-16 architecture, which consists of 16 layers, including convolutional layers with the ReLU activation function and pooling layers, specifically max pooling. The fully connected layer also employs the ReLU activation function, while the output layer uses the Softmax. The Viola-Jones method is used for facial detection in images, achieving an accuracy of 87.6% in locating faces. Meanwhile, the CNN method is applied for facial expression recognition, with an accuracy of 59.8% in classifying emotions.
Artificial Intelligence Untuk Identifikasi Motif Tenun Tradisional Sulawesi Tengah Pusadan, Mohammad Yazdi; Laila, Rahma; Pratama, Septiano Anggun
Bomba: Jurnal Pembangunan Daerah Vol 5 No 1 (2025)
Publisher : Badan Riset dan Inovasi Daerah Sulawesi Tengah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65123/bomba.v5i1.93

Abstract

Traditional weaving from Central Sulawesi, such as the motifs of Magau, Banua Oge/Souraja, and Tadulako, reflects deep cultural and historical values. However, the complexity of the patterns and motifs often makes manual identification challenging. This research employs an Artificial Intelligence (AI) approach using Convolutional Neural Networks (CNN) to automate the identification of these motifs. The AI model is trained using a diverse dataset of woven motif images and shows significant accuracy in classifying Magau, Banua Oge/Souraja, and Tadulako motifs. This research opens up cultural preservation and innovation opportunities in woven products with modern technology. The achieved result is the evaluation of the AI model using the following metrics: accuracy, precision, recall, and the confusion matrix. The accuracy obtained for each motif reaches 90%.
Implementation of Long Short-Term Memory Algorithms on Cryptocurrency Price Prediction with High Accuracy on Volatile Assets Nursiana Zasqia, Andi Nirina; Laila, Rahmah; Trezandy Lapatta, Nouval; Yazdi Pusadan, Mohammad; Santi, Dessy; Wirdayanti
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2422

Abstract

Cryptocurrencies have emerged as one of the most popular digital assets, characterized by high volatility, which presents a significant challenge in forecasting their price movements accurately. This study aims to implement the Long Short-Term Memory (LSTM) algorithm to predict the prices of selected cryptocurrencies, including Bitcoin (BTC), Binance Coin (BNB), Ethereum (ETH), Dogecoin (DOGE), Solana (SOL), and Shiba Inu (SHIB). The LSTM model is trained using the Adam optimizer and employs early stopping to mitigate overfitting. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results indicate that the LSTM model achieves strong predictive accuracy for relatively low-volatility assets such as Dogecoin and Solana, with R² scores of 0.9795 and 0.9523, respectively. In contrast, its performance declines when applied to highly volatile assets like Bitcoin and Binance Coin. The findings also suggest that LSTM performs best in short-to-medium-term forecasts (7 to 30 days), but shows limitations in long-term predictions. This study contributes to the field by demonstrating the applicability of LSTM in financial forecasting and highlighting its strengths and constraints across different volatility profiles. Practically, the findings can assist traders and financial analysts in making data-driven decisions by applying LSTM models for more reliable short-term predictions, while emphasizing the need to integrate external market factors to enhance long-term forecast accuracy.
Co-Authors Ahmad Imam Abdullah Amriana Andi Hendra Andriar Makahrun Anisa Yulandari Anisa Yulandari Anita Ahmad Kasim AR. Lamasitudju, Charunnisa Buliali, Joko Lianto Chairunnisa Lamasitudju Dandy Pramana Hostiadi Dessy Santi Dwi Shinta Dwi Shinta Angreni Dwi Shinta Angreni Dwiwijaya, Kadek Agus Eva Hariyanti Fahmi, Moh. Fatonah, Nenden Siti Fuad Mahfud Fuad Mahfud Ghifari, Ari Ginardi, Raden Venantius Hari Gunawan Feri Handono Hajra Rasmita Ngemba Handono, Gunawan Feri Harlin Feby Karnita Sumbaluwu Indah Safitri Irene Realyta Halldy Trosi Tangkawarow Julian Witjaksono Junus Widjaja Laila, Rahma Lamadjido, Moh. Raihan Dirga Putra Lamasitudju, Chairunnisa Magfirah, Magfirah Merry Miftah Miftah, Miftah Moh.Fajrin Sigit Aldy Muh Alif Alghifari Muh. Faried Muchtar Muh. Ilham Reonaldi Mutia Nanda, Rifqi Nouval Trezandy Lapatta Nursalim, Moh. Agung Nursiana Zasqia, Andi Nirina Ono, Jumaita Paloloang, Muhammad Fadhil Akmal B. Pramadinda, Alda Nur Pratama, Moh. Rezki Putra, Ryan Adi Rahma Laila Rahmah Laila Rahmah Laila Rifki, Moh Rizka Ardiansyah Rizka Ardiansyah Rumampuk, Viola Gracella Ryfial Azhar Ryfial Azhar Sabarudin Saputra Sahrullah Sahrullah Saleh, Muhammad Taufik Sani, Ilham Abdillah Sari Handayani Pusadan Sasuwuk, James Rio Septiano Anggun Pratama Siddiq Assegaf, Djafar Sondakh, Clivent Gerhard Suriyanti Syahrullah Syahrullah Syahrullah Syaiful Hendra Tafania Natalia Kasaedja Therry, Alvin Christian Davidson Wirdayanti Wirdayanti Wirdayanti Yulianti, Indira Yuri Yudhaswana Joefrie Yusuf Anshori Yusuf Anshori Zakiani Sakka