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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research ILKOM Jurnal Ilmiah Computer Based Information System Journal Jurnal Teknik Mesin Jurnal Ilmu Komputer dan Bisnis Jurnal Ekonomi Manajemen Sistem Informasi Indonesian Journal of Social Work Journal of Applied Data Sciences Jurnal Cahaya Mandalika International Journal of Computer and Information System (IJCIS) International Journal of Engineering, Science and Information Technology Djtechno: Jurnal Teknologi Informasi Jurnal Tika Jurnal Info Sains : Informatika dan Sains Malikussaleh Journal of Mechanical Science Technology Jurnal Ilmiah Kebijakan dan Pelayanan Pekerjaan Sosial (Biyan) J-Intech (Journal of Information and Technology) Jurnal Minfo Polgan (JMP) Jurnal Puan Indonesia Journal International of Lingua and Technology JUSIFOR : Jurnal Sistem Informasi dan Informatika Jurnal Desain dan Analisis Teknologi Innovative: Journal Of Social Science Research Priviet Social Sciences Journal Jurnal Pengabdian Ibnu Sina Journal of Society Bridge JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah Jurnal Pengabdian Barelang Jurnal Sistem Informasi dan Manajemen Jurnal Accounting Information System (AIMS) Jurnal Polimesin Computer & Science Industrial Engineering Journal Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK)
Tukino, Tukino
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Implementation of The Seasonal Autoregressive Integrated Moving Average Predictive Model on Raw Material Usage Data at PT. Plastik Karawang Flexindo Alfiansyah, Muhammad Rindra; Tukino, Tukino; Hananto, Agustia; Novalia, Elfina
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.867

Abstract

Fluctuations in raw material utilization in the manufacturing industry significantly impact production process efficiency, operational costs, and supply chain stability. Inaccurate planning and management of raw material inventories can lead to two extreme conditions: excess stock, which increases storage costs and the risk of expiration, or stock shortages, which could halt the production process and reduce productivity. To improve the accuracy of raw material consumption planning, this study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict raw material needs periodically based on historical data. The dataset used includes the consumption of Polyethylene (PE), High Density Polyethylene (HDPE), and Polypropylene (PP) from 2019 to 2025. The data is analyzed using a time series forecasting approach to identify trends and seasonal patterns. The SARIMA model is developed and optimized using three methods to search for the best parameters: Grid Search, Random Search, and Bayesian Optimization, to enhance prediction performance. The model's evaluation calculates the Mean Absolute Percentage Error (MAPE) as an accuracy indicator. The evaluation results show that although SARIMA can recognize seasonal patterns in raw material consumption, the prediction accuracy varies, with the best MAPE value being 16% and the highest being 34%. This indicates that external factors, such as market dynamics, government policies, global price fluctuations, and internal variables such as production schedules and customer demand, need to be considered to improve the model's precision. Overall, the application of SARIMA in this context provides a strategic contribution to supply chain management in the manufacturing industry, particularly in anticipating raw material needs, reducing uncertainty, and supporting more efficient and adaptive data-driven decision-making.
Implementasi Deteksi Objek Penggunaan Helm Dengan Metode YOLOv10 Reswara, Hadaya Abhista; Priyatna, Bayu; Hananto, Agustia; Tukino, Tukino
Jurnal Minfo Polgan Vol. 14 No. 1 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i1.15010

Abstract

Keselamatan berkendara, khususnya bagi pengendara sepeda motor, merupakan isu krusial, dan penggunaan helm secara konsisten terbukti dapat mengurangi risiko cedera kepala. Deteksi otomatis penggunaan helm melalui analisis citra dapat menjadi solusi efektif untuk memantau dan meningkatkan kepatuhan terhadap peraturan keselamatan. Dalam penelitian ini, model YOLOv10, sebuah arsitektur deteksi objek real-time terbaru, dilatih dan diuji menggunakan dataset citra yang relevan. Kinerja model dievaluasi berdasarkan metrik deteksi objek standar seperti precision, recall, F1-score, dan mean Average Precision (mAP). Berdasarkan hasil pelatihan dengan 300 citra dan 60 data validasi, model YOLOv10 berhasil mencapai nilai mAP50 sebesar 99,5%. Sementara itu, hasil pengujian menggunakan 20 citra menghasilkan akurasi sebesar 95%, menunjukkan bahwa sistem mampu mendeteksi penggunaan helm dengan cukup baik.
Klasifikasi Barang NG Berdasarkan Dataset Menggunakan Algoritma C.45 Studi Kasus: Perusahaan Manufaktur XYZ Jasmine Dina Sabila; Tukino, Tukino; Agustia Hananto
Jurnal Ekonomi Manajemen Sistem Informasi Vol. 6 No. 6 (2025): Jurnal Ekonomi Manajemen Sistem Informasi (Juli - Agustus 2025)
Publisher : Dinasti Review

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jemsi.v6i6.5644

Abstract

Perusahaan XYZ adalah perusahaan asal Jepang yang bergerak di bidang otomotif, khususnya dalam produksi suku cadang. Produk utamanya adalah perakitan selang untuk sistem pendingin udara (air-conditioning hose assemblies) untuk kendaraan Original Equipment Manufacturer (OEM). Departemen produksi Final Inspection bertugas memastikan bahwa barang produksi memenuhi standar kualitas sesuai dengan Work Instruction (WI) dan spesifikasi dimensi berdasarkan gauge. Dalam pelaksanaan tugasnya, apabila ditemukan Not Good (NG), barang tersebut akan dipisahkan dari barang OK, kemudian ditandai dan disisihkan untuk dianalisis lebih lanjut. Pencatatan barang Not Good (NG) ini dilakukan melalui Website Worktop, Dimana data dapat diakses kapan saja dan mencakup jenis, jumlah, serta masalah NG (not good). Sistem ini membantu pelaporan real-time dan mempermudah pemantauan data produksi. Dari hasil input logbook, ditemukan adanya NG (not good) yang berulang pada proses produksi. Kondisi NG (not good) yang berulang ini menyebabkan gangguan pada waktu cycle time, sehingga berdampak pada efisiensi produksi. Data hasil input logbook yang belum diolah menjadi knowledge dapat membantu membuat keputusan departemen produksi dengan mengklasifikasikan data NG (not good) menjadi kategori keparahan NG (not good) menggunakan data mining diharapkan dapat membentuk pola untuk mendukung pengambilan keputusan yang baik.
KLASIFIKASI ULASAN APLIKASI KOPI KENANGAN PADA GOOGLE PLAYSTORE MENGGUNAKAN ALGORITMA NAIVE BAYES Fadli, Muhammad Abil; tukino, Tukino; Novalia, Elfina; Hananto, April Lia
Djtechno: Jurnal Teknologi Informasi Vol 6, No 2 (2025): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i2.7037

Abstract

Aplikasi Kopi Kenangan merupakan aplikasi yang digunakan untuk pemesanan minuman secara online milik perusahaan PT Bumi Berkah Boga. Selain hal tersebut aplikasi ini juga membantu perusahaan menerima ulasan terkait pengalaman pelanggan dalam menggunakan layanan aplikasi kopi kenangan. Namun ulasan pelanggan di Google Playstore memiliki jumlah data yang banyak sehingga sulit dianalisis secara manual. Tujuan penelitian ini melakukan klasifikasi ulasan pelanggan pada aplikasi Kopi Kenangan mempergunakan algoritma Naïve bayes. Metode penelitian ini meliputi pengumpulan data, preprocessing, pemodelan dan evaluasi. Data yang dipergunakan dalam penelitian ini yaitu sebesar 1000 data dengan lima kategori yaitu promo, pelayanan, performa aplikasi, transaksi dan kualitas produk. Hasil penelitian menunjukkan bahwa kategori ulasan terbanyak adalah tentang performa aplikasi dengan persentase 45% dari total 1000 data ulasan. Hasil akurasi penelitian yaitu sebesar 85% yang menunjukkan bahwa model dapat melakukan klasifikasi data kategori sentimen dengan cukup baik.
KLASIFIKASI TEXT ULASAN PENGGUNA APLIKASI WONDR BY BNI MENGGUNAKAN ALGORITMA NAIVE BAYES Sari, Fitria Ratna; Tukino, Tukino; Hilabi, Shofa Shofiah; Priyatna, Bayu
Djtechno: Jurnal Teknologi Informasi Vol 6, No 2 (2025): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i2.6819

Abstract

Penelitian ini berfokus pada proses klasifikasi ulasan pengguna aplikasi Wondr by BNI dengan menerapkan algoritma Naïve Bayes. Data yang digunakan berasal dari platform Kaggle, terdiri dari 1.500 data ulasan pengguna aplikasi yang telah melewati tahapan pre-processing seperti cleansing, tokenization, transform cases, stopwords, dan filter tokens. Ulasan tersebut kemudian diberi label secara manual ke dalam kategori label cepat, biasa saja, lambat, dan tidak responsif. Setelah itu label akan di buat otomatis oleh Naïve Bayes. Dataset dibagi menjadi 80:20, lalu di proses menggunakan model klasifikasi berbasis probabilistik Naïve Bayes. Hasil pengujian menunjukkan bahwa algoritma Naïve Bayes mampu mengklasifikasikan ulasan pengguna dengan tingkat akurasi sebesar 95%. Evaluasi model berdasarkan precision, recall, dan f1-score menunjukkan performa klasifikasi yang sangat baik pada setiap kategori ulasan. Visualisasi hasil klasifikasi menggunakan confusion matrix, diagram batang, dan wordcloud memberikan pemahaman lebih mendalam terhadap pola ulasan pengguna. Temuan ini membuktikan bahwa algoritma Naïve Bayes efektif dalam menangani teks tidak terstruktur dan dapat diandalkan untuk mendukung analisis evaluasi layanan digital berbasis umpan balik pengguna.Kata Kunci: Klasifikasi Teks, Naïve Bayes, Ulasan Pengguna, Kaggle, Wondr by BNI
Psychosocial transformation of the deaf community through inclusive empowerment programs: A case study of the PERINTIS CSR Program at PT. Kilang Pertamina International RU VI Balongan Mildawati, Milly; Hekmatyar, Versanudin; Subarkah, Ade; Kuswanda, Dede; Tukino, Tukino; Wibisosno, Eko Gunawan; Arsyad, Fachry; Zulkifli, Mohamad; Purnomo, Andromedo Cahyo; Kusuma P, ⁠Shafira Putri
Priviet Social Sciences Journal Vol. 5 No. 9 (2025): September 2025
Publisher : Privietlab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55942/pssj.v5i9.700

Abstract

The deaf community in Indonesia continues to face psychosocial challenges that extend beyond communication barriers, including low self-esteem, limited social participation, and social stigma. While many Corporate Social Responsibility (CSR) initiatives focus on economic outcomes, little is known about their impact on psychosocial well-being. This study explores the psychosocial transformation experienced by members of the Deaf community through the PERINTIS Program, a CSR initiative of PT. Kilang Pertamina International RU VI Balongan, Indonesia. Using a qualitative case study approach, data were collected through in-depth interviews with the program participants, parents, and the implementation team. A three-stage thematic analysis (open, axial, and selective coding) was conducted. The findings indicate that prior to joining the program, the participants often showed signs of withdrawal, low confidence, and emotional instability. After engaging in barista training and mentoring within an inclusive community space, the participants reported increased confidence, stronger social relationships, improved emotional regulation, and greater motivation for the future. These changes were facilitated by a strengths-based, gradual empowerment process supported by mentors and an inclusive social environment. This study highlights the importance of integrating psychosocial dimensions into CSR initiatives, showing that empowerment goes beyond technical skill-building to include identity reconstruction and social recognition. The results contribute to disability studies, social work practice, and CSR policy and suggest that similar community-based models may foster more sustainable and inclusive empowerment.
Application of Convolutional Neural Networks for Automated Iris Edge Detection in Sleepiness Monitoring during Blended Learning Tukino, Tukino; Yuhandri, Yuhandri; Sumijan, Sumijan
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.882

Abstract

This study introduces a novel lightweight Convolutional Neural Network (CNN) model, T-Net, designed for real-time drowsiness detection based on eye closure patterns. The model was developed to address the prevalent issue of student fatigue in resource-constrained environments, such as during prolonged online learning or blended learning sessions. Unlike traditional deep learning models, T-Net prioritizes efficiency while maintaining high accuracy, making it suitable for deployment on devices with limited computational resources. The model uses a 68-point facial landmark detection technique to extract the eye region and accurately classify eyelid states (open or closed). Evaluated on two benchmark datasets, Dataset-1 (342 eye images) and Dataset-2 (1,510 eye images), T-Net demonstrated superior performance, achieving classification accuracies of 99.33% and 99.27%, respectively, outperforming other pre-trained models such as VGG19, ResNet50, and MobileNetV2. Usability testing revealed a high acceptance rate, with a System Usability Scale (SUS) score of 84.5, indicating the system’s practicality for real-world use. Additionally, statistical analysis showed a significant correlation (r = 0.67, p 0.01) between prolonged screen time and the emergence of visual fatigue symptoms. This study highlights the effectiveness of a lightweight CNN approach for real-time fatigue monitoring, offering a balance between performance and computational efficiency. The results suggest that T-Net can be effectively integrated into student monitoring systems to ensure alertness during learning sessions. Future research will focus on expanding the dataset, integrating infrared imaging for low-light environments, and incorporating additional fatigue indicators such as yawning and head pose.
Blockchain Application On Independent Smart Agriculture Hilabi, Shofa Shofiah; Fauzi, Ahmad; Tukino, Tukino
International Journal of Artificial Intelligence Research Vol 7, No 1.1 (2023)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.1.988

Abstract

The agricultural supply chain is currently facing challenges such as lack of transparency, uncertainty in product origin, and difficulty in accurately tracking products. This article discusses the application of blockchain technology as a solution to enhance agricultural supply chain management. It analyzes how blockchain can improve transparency, reliability, and security in agricultural supply chain management by recording and verifying information in a decentralized manner. Through blockchain, information such as product origin, production methods, shipping details, and storage conditions can be easily traced and verified by the involved parties. The implementation of blockchain also enables smart contracts to automatically execute agreements and payments based on predefined conditions, reducing bureaucracy and enhancing efficiency. The article also addresses challenges in implementing blockchain in the agricultural supply chain, such as data standardization and collaboration among stakeholders. By implementing blockchain technology, it is expected to create a more transparent, efficient, and trusted agricultural supply chain, benefiting farmers, producers, distributors, and consumers by ensuring product authenticity, improving compliance with quality standards, and minimizing the risks of counterfeiting or contamination.  
The Rekayasa Teknologi Konseling Kelompok dengan Relaksasi Otot Progresif dalam Menurunkan perubahan suasana hati (mood swing) pada lanjut usia di Panti Sosial Bina Insan Bangun Daya 2 Jakarta Timur Nahampun, Mawi; Sukoco, Dwi Heru; Tukino, Tukino
Jurnal Ilmiah Kebijakan dan Pelayanan Pekerjaan Sosial (Biyan) Vol 6 No 2 (2024): BIYAN
Publisher : Politeknik Kesejahteraan Sosial (Poltekesos) Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31595/biyan.v6i2.1293

Abstract

Group Counseling with Progressive Muscle Relaxation is the result of psychosocial therapy technology engineering in the form of modifying group counseling therapy steps to be more effective with the involvement of progressive muscle relaxation, namely stretching the physical muscles of the elderly who experience mood swing problems at the Bina Insan Bangun Daya 2 Social Home in East Jakarta. Group Counseling with Progressive Muscle Relaxation is used to deal with the problems of the elderly who experience mood swings during group counseling. This study aims to explain the implementation of group counseling therapy with progressive muscle relaxtation on reducing mood swings in the elderly. This study uses a Single Subject Design (SSD) type of A-B-A reversal. The subject in this study were JN, AF, and SL. The target behavior observed in this study during group counseling activities was related to changes in mood (mood swings) in the elderly, namely exticed conditions, feelings of fatigue, feelings of anger, and relaxed and calm conditions. The validity test of the research instrument used face validity and the reliability test used percent agreement. Then the data analysis used was visual data analysis consisting of analysis in conditions and between conditions. The results of the study indicate that Group Counseling Therapy with Progressive Muscle Relaxation has been proven to be able to reduce mood swings in the elderly, which means that Group Counseling Therapy with Progressive Muscle Relaxation has an effect on reducing mood swings in research subjects, which is known through data trend analysis with increasing and decreasing trends in the percentage of overlapping data in the analysis between conditions below 50% because the smaller the percentage of overlapping data, the stronger the influence of the intervension on changes in treatment.
Optimization of Machine Learning Models with Segmentation to Determine the Pose of Cattle Siregar, Amril Mutoi; Hartono Wijaya, Sony; Fauzi, Ahmad; Sen, Tjong Wan; Faisal, Sutan; Tukino, Tukino; Cahyana, Yana
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26750

Abstract

Image pattern recognition poses numerous challenges, particularly in feature recognition, making it a complex problem for machine learning algorithms. This study focuses on the problem of cow pose detection, involving the classification of cow images into categories like front, right, left, and others. With the increasing popularity of image-based applications, such as object recognition in smartphone technologies, there is a growing need for accurate and efficient classification algorithms based on shape and color. In this paper, we propose a machine learning approach utilizing Support Vector Machine (SVM) and Random Forest (RF) algorithms for cow pose detection. To achieve an optimal model, we employ data augmentation techniques, including Gaussian blur, brightness adjustments, and segmentation. The proposed segmentation methods used are Canny and Kmeans. We compare several machine learning algorithms to identify the optimal approach in terms of accuracy. The success of our method is measured by accuracy and Receiver Operating Characteristic (ROC) analysis. The results indicate that using the Canny segmentation, SVM achieved 74.31% accuracy with a testing ratio of 90:10, while RF achieved 99.60% accuracy with the same testing ratio. Furthermore, testing with SVM and K-means segmentation reached an accuracy of 98.61% with a test ratio of 80:20. The study demonstrates the effectiveness of SVM and Random Forest algorithms in cow pose detection, with Kmeans segmentation yielding highly accurate results. These findings hold promising implications for real-world applications in image-based recognition systems. Based on the results of the model obtained, it is very important in pattern recognition to use segmentation based on color even though shape recognition.
Co-Authors AA Sudharmawan, AA Agustina, Alvi Ahmad, Sandi Ahnaf, Naufal Zubdi Akbari, Wahyu Azriel Akmal, Khafid Khaulsar Alfiah, Agry Alfiansyah, Muhammad Rindra Algifanri Maulana, Algifanri Amalia Amalia Amrizal Amrizal Angeli, Alvin Annam, Dyno Syaiful Apriani, Fitria April Lia Hananto Arif Rahman Hakim Arnomo, Sasa Ani Arsyad, Fachry Aulia, Aldi Azwanti, Nurul Baru Harahap Berkah*, Kamila Catur Nugroho ceni kirani valensyah Danny Manongga Deddy Prihadi Dede Kuswanda Dien Noviany Rahmatika Dzulqarnain, Fahmi Eichler, Luiz Elisa, Erlin Fadli, Muhammad Abil Faisal, Sutan Fajrin, Alfannisa Fauzi Ahmad Muda Ferdiansyah, Indra Fifi, Fifi FIKRI HAIKAL Guntur, Muhamad Hananto, Agustia Handayani, Citra Handoko, Koko Harman, Rika Hartono Wijaya, Sony Hendry Hilabi, Shofa Shofiah Hindriyanto Dwi Purnomo Huda, Baenil I Gede Iwan Sudipa Irawan, Bei Harira Irwan Sembiring Iwan Setiawan Jasmine Dina Sabila Karyadi Karyadi Kurnia, Nisa Kusuma P, ⁠Shafira Putri Leony, Alvina Lindo, Junius Manalu, Soli Vernika Mildawati, Milly Muammar Khaddafi Mubarok, Piky Muhammad, Daniel Muslih, Muhamad Nahampun, Mawi Nanda, Rizki Aulia Nofriani Fajrah, Nofriani Novalia, Elfina Novaria, Rachmawati Nurapriani, Fitria Oganda, Decut Della Pandiangan, Satria Patya, Dhea Intan PERNANDO, Pernando Pratama, Daffa Agung Pratiwi, Mutiana Priatna, Bayu Priyatna, Bayu Purba, rehni jayana Purnomo, Andromedo Cahyo Putria, Narti Eka Ramadhan, Muhammad Faiz Reswara, Hadaya Abhista Rivai, Samuel Saepul Aripiyanto Samosir, Epa Prima Melina Sari, Fitria Ratna Sari, Nilah Wati Indra Nur Meinda Sari Sarjon Defit Setianingsih, Krisna Dewi Sianturi, Nico Bangun Rezkyanto Silva, Tiago Simanjutak, Pastima Siregar, Amril Mutoi Situmorang, Awaljan Soleman, Soleman Sri Wahyuni Subarkah, Ade Suhara, Ade Sukoco, Dwi Heru Sulestra, Ikhwan Sumijan Sumijan Syahril Effendi Syamsiar, Syamsiar Syelfiyananda, Syelfiyananda Tiodora, Jeremy Tjong Wan Sen Triandy, Oky Versanudin Hekmatyar Wibisosno, Eko Gunawan Wong, Hendri Yana Cahyana Yohanna Siahaan, Winda Yuhandri Yuhandri, Yuhandri Zetli, Sri Zulkifli, Mohamad