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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) The Indonesian Journal of Computer Science Jurnal Informatika dan Kesehatan
Mohammad Yazdi Pusadan
Program Studi Teknik Informatika, Universitas Tadulako, Palu, Indonesia
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Public Health Other

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Pengenalan Batik Bomba Menggunakan Teknologi Augmented Reality Dengan Metode Markerless Berbasis Android Kasaedja, Tafania Natalia; Kasim, Anita Ahmad; Pusadan, Mohammad Yazdi; Syahrullah, Syahrullah; Laila, Rahmah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6128

Abstract

Batik Bomba merupakan kain tradisional khas suku Kaili yang menjadi salah satu kekayaan Sulawesi Tengah. Motif dan pola batik Bomba memiliki bentuk yang unik, dengan makna filosofis yang berlandaskan kehidupan masyarakat suku Kaili yang tersirat didalamnya. Namun pemahaman tentang ragam motif batik Bomba belum dikenal luas oleh masyarakat Sulawesi Tengah khususnya Kota Palu. Hal ini disebabkan karena media informasi untuk visualisasi kain batik Bomba masih kurang, umumnya hanya berbentuk gambar 2D yang dapat ditemui di museum atau pameran seni. Dari permasalahan tersebut, penulis bertujuan untuk memberikan informasi kepada masyarakat lokal maupun masyarakat luar mengenai filosofi motif batik Bomba secara detail dan mudah dipahami dengan memanfaatkan media teknologi Augmented Reality menggunakan metode markerless yang menampilkan objek 3D batik Bomba. Dalam pengembangan aplikasi, penulis menggunakan metode agile Extreme Programming (XP) yang akan diimplementasikan kedalam aplikasi berbasis android. Diperoleh hasil analisis pengujian menggunakan metode Blackbox Testing yang dilakukan oleh develop, dan User Acceptance Testing (UAT) melalui kuesioner yang dibagikan kepada pengguna aplikasi, bahwa aplikasi yang dikembangkan berjalan sesuai dengan fungsionalitasnya dan memperoleh skor rata-rata 107,25 (Sangat Memuaskan). Dengan demikian, aplikasi AR About Bomba dapat menjadi mediator pengenalan filosofi setiap motif batik Bomba.
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.
KLASIFIKASI JENIS BATIK BOMBA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DENGAN ARSITEKTUR EFFICIENT-NET B2 (BATIK BOMBA SULAWESI TENGAH ) Witjaksono, Julian; Pusadan, Mohammad Yazdi; Anshori, Yusuf; Ardiansyah, Rizka; Azhar, Ryfial
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.6191

Abstract

Batik adalah salah satu warisan budaya Indonesia yang diakui oleh UNESCO sebagai warisan dunia. Keanekaragaman motif batik mencerminkan kekayaan budaya dan seni yang perlu dilestarikan. Salah satu motif batik yang unik adalah batik Bomba dari Kabupaten Donggala, Sulawesi Tengah. Untuk membantu mengklasifikasikan motif batik yang beragam, penelitian ini menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur EfficientNet-B2. Penelitian ini melibatkan pengumpulan 21 citra batik Bomba dari berbagai sumber di Kota Palu, Sulawesi Tengah. Proses data preprocessing dilakukan melalui teknik augmentasi data, sementara model dikembangkan dengan menggunakan transfer learning dan beberapa teknik optimisasi seperti batch normalization, regulasi, dropout layer, dan fungsi aktivasi ReLU serta softmax. Model dilatih dengan optimizer Adamax dan early stopping untuk mencegah overfitting. Hasil pelatihan menunjukkan akurasi tinggi sebesar 100% pada data pelatihan dan 99.59% pada data validasi. Pengujian menggunakan confusion matrix menunjukkan akurasi total model sebesar 96%, dengan kesalahan klasifikasi minimal pada gambar "maleo". Model ini berhasil mengklasifikasikan motif batik Bomba dengan tingkat akurasi yang tinggi, menunjukkan potensi besar penggunaan teknologi kecerdasan buatan dalam pelestarian dan pengembangan warisan budaya batik.
PENERAPAN CONVOLUTION NEURAL NETWORK (CNN) UNTUK DETEKSI MEGALITIKUM DI SULAWESI TENGAH BERBASIS MOBILE Fahmi, Moh.; Laila, Rahma; Pusadan, Mohammad Yazdi; Syahrullah, Syahrullah; Azhar, Ryfial; Sani, Ilham Abdillah; Magfirah, Magfirah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.6458

Abstract

Taman Nasional Lore Lindu di Sulawesi Tengah, Indonesia, memiliki berbagai objek megalitikum, termasuk arca, kalamba, lumpang, dan batu dulang. Kawasan ini memiliki potensi untuk secara resmi diakui sebagai Situs Warisan Dunia, namun pengguna masih menghadapi tantangan dalam mengidentifikasi dan memahami artefak megalitikum ini. Sebagai tanggapan atas masalah ini, penelitian ini telah menciptakan sistem atau aplikasi yang menggunakan algoritma CNN (Convolutional Neural Network) dengan platform Teachable Machine untuk meningkatkan kemampuan pengguna dalam mengidentifikasi objek megalitikum. Program ini akan menawarkan informasi yang lebih luas untuk setiap objek megalitikum, termasuk penggunaan yang dimaksudkan dan konteks sejarahnya. Temuan uji menunjukkan bahwa program ini memiliki kemampuan untuk mengidentifikasi objek megalitikum dengan tingkat akurasi hingga 98%. Selain itu, pengguna dapat dengan mudah mengakses informasi yang lebih komprehensif tentang artefak-artefak ini. Program ini memungkinkan pengguna untuk dengan mudah mengidentifikasi dan memahami objek megalitikum, sambil juga memberikan mereka informasi yang lebih mendalam tentang artefak-artefak tersebut.
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.
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.
Co-Authors Ahmad Imam Abdullah Aldy, Moh.Fajrin Sigit Andriar Makahrun Anita Ahmad Kasim Buliali, Joko Lianto Chairunnisa Ar. Lamasitudju Dandy Pramana Hostiadi Dwi Shinta Angreni Eva Hariyanti Fahmi, Moh. Fatonah, Nenden Siti Ghifari, Ari Ginardi, Raden Venantius Hari Gunawan Feri Handono Hajra Rasmita Ngemba Handono, Gunawan Feri Indah Safitri JULIAN WITJAKSONO Kadek Agus Dwiwijaya Karnita Sumbaluwu, Harlin Feby Kasaedja, Tafania Natalia Laila, Rahma Lamadjido, Moh. Raihan Dirga Putra Lamasitudju, Chairunnisa Magfirah, Magfirah Merry Miftah Miftah, Miftah Mutia Nanda, Rifqi Nouval Trezandy Lapatta Nursalim, Moh. Agung Putra, Ryan Adi Rahmah Laila Rifki, Moh Rizka Ardiansyah Rumampuk, Viola Gracella Ryfial Azhar, Ryfial Sahrullah Sahrullah Sani, Ilham Abdillah Sari Handayani Pusadan Sasuwuk, James Rio Septiano Anggun Pratama Sondakh, Clivent Gerhard Suriyanti Syahrullah Syahrullah Syahrullah Tangkawarow, Irene Therry, Alvin Christian Davidson Wirdayanti Yudhaswana, Yuri Yuri Yudhaswana Joefrie Yusuf Anshori Zakiani Sakka