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All Journal INTECOMS: Journal of Information Technology and Computer Science Jurnal Sains dan Teknologi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) INFORMASI (Jurnal Informatika dan Sistem Informasi) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pengabdian Nasional (JPN) Indonesia Journal Innovations Computer Science
Poerwandono, Edhy
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences Other

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Implementasi 3D Bin Packing Problem Menggunakan Algoritma Tabu Search Poerwandono, Edhy; Sultan Faqih Fiddin
Jurnal Sains dan Teknologi Vol. 5 No. 1 (2023): Jurnal Sains dan Teknologi
Publisher : CV. Utility Project Solution

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Abstract

Ruang penyimpanan merupakan salah satu tantangan dalam industri logistik, terutama dalam pengemasan dan pengirimanan barang pada kontainer. Perusahaan logistik umumnya membutuhkan solusi yang efektif untuk mengoptimalkan ruang yang tersedia pada kontainer. Terdapat banyak metode dan pendekatan untuk masalah ini, salah satunya menggunakan algoritma tabu search yang dapat digunakan untuk memecahkan masalah ini. Tujuan dari penelitian ini adalah untuk mengimplementasikan algoritma tabu search dan menampilkan hasilnya yang akan menggunakan gambar 3 dimensi. Hasil dari penelitian ini menunjukkan bahwa metode tabu search dapat mengoptimalkan penggunaan ruang penyimpanan dan dengan menampilkan gambar 3 dimensi, penerapan di lapangan akan sangat mudah. Dari hasil pengujian, algoritma tabu search menunjukkan kinerja yang lebih baik dalam hal optimasi ruang penyimpanan dan efisiensi biaya. Oleh karena itu, perusahaan logistik kargo dapat mempertimbangkan penggunaan metode tabu search untuk meningkatkan efisiensi dan produktivitas memuat barang ke dalam kontainer.
Penerapan Data Mining Untuk Penilaian Kinerja Karya Di PT Riksa Dinar Djaya Menggunakan Metode Naive Bayes Classification Poerwandono, Edhy; Perwitosari, Faizal Joko
Jurnal Sains dan Teknologi Vol. 5 No. 1 (2023): Jurnal Sains dan Teknologi
Publisher : CV. Utility Project Solution

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Abstract

Menurut Undang-Undang Tahun 1969 tentang Ketentuan-Ketentuan Pokok Mengenai Tenaga Kerja dalam pasal 1 dikatakan bahwa karyawan adalah tenaga kerja yang melakukan pekerjaan dan memberikan hasil kerjanya kepada pengusaha yang mengerjakan dimana hasil karyanya itu sesuai dengan profesi atau pekerjaan atas dasar keahlian sebagai mata pencariannya. Senada dengan hal tersebut menurut Undang-Undang No.14 Tahun 1969 tentang Pokok Tenaga Kerja, tenaga kerja adalah tiap orang yang mampu melaksanakan pekerjaan, baik di dalam maupun diluar hubungan kerja guna menghasilkan jasa atau barang untuk memenuhi kebutuhan masyarakat Dalam penelitian ini dilakukan pemodelan data mining dengan menggunakan algoritma Naive Bayes untuk mendapatkan langkah - langkah sistematis dalam menilai kinerja karyawan di PT. Riksa Dinar Djaya. Hal ini dilakukan untuk mengetahui kedisiplinan karyawan. Berdasarkan hasil yang di peroleh dari Rapidminer menggunakan algoritma Naïve Bayes menghasilkan nilai label dari karyawan dengan Jabatan Maintenance pada studi kasus di PT. Riksa Dinar Jaya yaitu disiplin 0.397 dan tidak dispilin 0.420 menunjukan bahwa karyawan dengan Jabatan ini paling sering terlambat dibanding dengan karyawan dengan Jabatan lainnya dengan hasil akurasi 100%. Dengan demikian penerapan algoritma Naive Bayes dapat dijadikan alternatif untuk pengambilan keputusan dalam menilai kinerja karyawan di PT. Riksa Dinar Djaya dan diharapkan karyawan yang belum memiliki hasil kinerja yang bagus dapat memperbaiki hasil kinerja dari kebijakan yang diambil perusahaan.
Implementasi Algoritma You Only Look Once (YOLOv8) untuk Mendeteksi Pelanggaran Lalu Lintas Berupa Tidak Menggunakan Helm (Studi Kasus di Jatiasih, Bekasi) Poerwandono, Edhy; Barronzoeputra, Gaoeng Qalbun
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.1017

Abstract

This research aims to implement the You Only Look Once (YOLO) algorithm in detecting traffic violations in the form of riders who do not use helmets in Jatiasih, Bekasi. The problem studied is the high number of violations in the form of riders who do not use helmets which play a vital role in protecting riders from danger. The expected solution is the development of an automatic detection system that is able to identify this offence with a high level of accuracy. The object of the research is motorcyclists in Jatiasih, Bekasi. The research method used includes applying the YOLO algorithm to video recordings from surveillance cameras at several monitoring points. Video data processing is done by a laptop and YOLO algorithm-based software. The results of this research are expected to support law enforcement efforts, increase awareness of helmet use, and improve road safety by building an automatic detection system that can identify offences such as not wearing a helmet. The results of this research will show how effective the YOLO algorithm is for spotting traffic offences.
Implementasi Sistem Antrian Pasien Berbasis Website Pada Klinik Sehat Tamba Kelurahan Cilangkap Poerwandono, Edhy; Anwar, Anggit Saepul; Mutia, Selvi; Damayanti, Yulia
Jurnal Pengabdian Nasional (JPN) Indonesia Vol. 5 No. 2 (2024): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jpni.v5i2.677

Abstract

Clinics are health facilities that provide basic medical services, diagnosis, treatment, and care to patients with minor or non-emergency medical conditions. The development of information technology has triggered the need for information systems in the medical field to improve health services. One important factor in increasing patient satisfaction is minimizing waiting time. Long queues can reduce clinic efficiency and affect patient satisfaction. This study aims to implement a website-based patient queue system at the Tamba Sehat Clinic, with the hope of increasing service efficiency and patient satisfaction. In implementing the website queuing system, researchers used qualitative methods to understand the effectiveness of health services and patient experiences. Data was collected through interviews, observations, and literature studies. The implementation of the Website-Based Patient Queuing System at the Tamba Sehat Clinic has had a significant impact. People can now easily get a queue number online via mobile phone or computer, eliminating the need to queue directly at the clinic location. Tamba Sehat Clinic, which was established in January 2023, has seen increased efficiency with this system. Patients can quickly and efficiently get a queue number, ensuring that their time is spent productively. Additionally, adopting this technology helps clinics improve the quality of service to prospective patients, creating a more comfortable and efficient experience for those in need of healthcare services. Thus, the implementation of a website-based patient queue system has brought real benefits to the community and clinics.
Analisis Data Sentimen Perbandingan Terhadap Game Online Mobile Legends dan PUBG Mobile Berdasarkan Tanggapan Masyarakat X Menggunakan Algoritma Naïve Bayes Rohmansyah, Fadillah Ali; Poerwandono, Edhy
INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 5 (2024): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v7i5.11824

Abstract

Mobile legends dan PUBG Mobile adalah game online yang sangat popular. Dengan berkembangnya industri game dan meningkatnya pengguna game online, penting untuk memahami bagaimana sentimen masyarakat merespon kedua game ini. Analisis sentimen memberikan wawasan berharga mengenai pandangan dan pengalaman pengguna yang dapat digunakan oleh pengembang untuk meningkatkan kualitas game. Oleh karena itu pada penelitian ini, akan mengambil komentar serta ulasan dari X dengan kata kunci pencarian Mobile legends dan PUBG Mobile untuk diolah dan mengklasifikasikan teks dengan menggunakan metode analisa sentimen. Data dikumpulkan dari media sosial X yang berisi tanggapan dan ulasan masyarakat sebanyak 2000 data tentang Mobile Legends dan PUBG Mobile. Proses analisis sentimen ini dilakukan dengan klasifikasi teks dibagi menjadi dua kelas yaitu kelas sentimen positif dan kelas sentimen negatif. Algoritma Naïve Bayes diterapkan untuk mengklasifikasikan sentimen menjadi positif dan negatif.
Analisis Clustering Penyakit Menular pada Manusia di Jakarta Timur Menggunakan Algoritma K-Means Ramadhan, Muhammad Arya; Poerwandono, Edhy; Akbar, Yuma; Hidayat, Aditya Zakaria
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 1 (2025): JANUARI-MARET 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i1.3007

Abstract

Humans are highly susceptible to various diseases without realizing their causes. The high incidence of infectious diseases in East Jakarta requires an analysis of distribution patterns to determine intervention priorities. This study aims to identify clusters of infectious diseases in East Jakarta, helping authorities plan effective prevention and treatment strategies. Data on infectious disease cases were obtained from the Central Statistics Agency of DKI Jakarta. The K-Means algorithm was used to cluster data based on variables such as period, region, type of disease, and number of cases. The results indicate several main clusters with distinct characteristics that can serve as a foundation for targeted strategies. From 2018 to 2021, diarrhea was predominant, making up 84.14% of cases in 2018 and 81.97% in 2019, pneumonia accounted for 32.92% in 2020, and TB Paru 33.63% in 2021. In conclusion, the K-Means algorithm effectively clusters infectious disease data and provides useful insights into disease distribution in East Jakarta, improving the impact of data-driven health programs.
Identification of Flower Type Images Using KNN Algorithm with HSV Color Extraction and GLCM Texture Poerwandono, Edhy; Taufik, M. Endang
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 3 (2025): JULI-SEPTEMBER 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i3.3826

Abstract

Due to the variety of types of flowers that exist and having and tracking each variety, making plant lovers and cultivators difficult to distinguish in determining the type of flower, it takes a very long time to find out the type of flower if you only rely on the five senses. With the application of the K-Nearest Neighbor algorithm and feature extraction of color and texture, it is very helpful in image processing to identify flowers more easily and shorten the time, with the greatest accuracy of 71% using the K-7 value, the flower was successfully carried out.
Implementasi Sistem Informasi Presensi Berbassis Web di TK Nailus Sa’adah Poerwandono, Edhy; Nabilah, Laila; Salfa Dhiyaa Azzizah, Putri; Maharani, Delia
INFORMASI (Jurnal Informatika dan Sistem Informasi) Vol 17 No 1 (2025): INFORMASI (Jurnal Informatika dan Sistem Informasi)
Publisher : LPPM STMIK Indonesia Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37424/informasi.v17i1.355

Abstract

TK Nailus Sa’adah menghadapi kendala dalam pencatatan presensi yang masih dilakukan secara manual, menyebabkan inefisiensi, kesalahan pencatatan, serta sulitnya pengelolaan data kehadiran siswa dan guru. Penelitian ini bertujuan untuk mengem- bangkan sistem informasi presensi berbasis web guna meningkatkan efisiensi admin- istrasi dan akurasi data. Metode penelitian yang digunakan adalah kualitatif deskriptif, dengan teknik pengumpulan data melalui observasi dan wawancara. Sistem dikem- bangkan menggunakan metode Waterfall, dengan tahapan analisis kebutuhan, perancangan, implementasi, pengujian, dan pemeliharaan. Teknologi yang digunakan mencakup framework Laravel dan database MySQL, serta fitur pencatatan otomatis, manajemen pengguna, dan pembuatan laporan real-time. Pengujian sistem dilakukan menggunakan black-box testing dan user acceptance testing (UAT), dengan hasil menunjukkan sistem berjalan sesuai kebutuhan pengguna. Implementasi sistem ini di- harapkan dapat mengatasi permasalahan administrasi presensi, mendukung digitalisasi di lingkungan pendidikan anak usia dini, serta meningkatkan kualitas layanan di TK Nailus Sa’adah.
Optimization of SMOTE Application for Classification Accuracy of Heart Disease Risk Using Artificial Neural Network Ibnu Sarky, Fauzan; Poerwandono, Edhy
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.302

Abstract

Heart disease remains a leading cause of mortality worldwide, including in Indonesia, and is often difficult to detect at an early stage. One of the main challenges in the Indonesian healthcare system is the lack of fully digitalized data management and the issue of imbalanced patient datasets, which reduce classification accuracy. This study developed a web-based information system designed to manage patient records and automatically classify heart disease risk. The system was implemented using the CodeIgniter framework with a MySQL database, and applied an Artificial Neural Network (ANN) in combination with the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. A total of 60 secondary patient records were processed through preprocessing, data balancing, model training, and cross-validation. Experimental results demonstrated that the application of SMOTE improved model sensitivity, with performance metrics of 87.4% accuracy, 85.2% precision, 88.6% recall, and an AUC-ROC of 0.94. These findings confirm that integrating ANN and SMOTE into a web-based system enhances classification reliability and supports faster medical decision-making. However, the study also acknowledges certain limitations, including the restricted dataset size and the absence of validation in real clinical environments. Future work should expand the dataset, test the system in healthcare facilities, and compare performance with other algorithms such as Random Forest or SVM to identify the most optimal predictive model.
Mobile-Based Real-Time Ornamental Rose Classification System Using YOLOv8 Algorithm on Digital Imagery Achmad Fahrezi, Irgy; Poerwandono, Edhy
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.339

Abstract

This research introduces a mobile-based system for real-time identification of ornamental rose varieties using the YOLOv8 deep learning algorithm. Motivated by the growing interest in ornamental plants during the COVID-19 pandemic and the high penetration of smartphone users in Indonesia, the study aims to create an efficient and accessible flower recognition tool. A dataset of 813 labeled rose images—red, white, yellow, orange, and pink—was collected from the Roboflow platform and processed using data augmentation techniques to improve model generalization. The YOLOv8 model was trained with 100 epochs, a batch size of 16, and the SGD optimizer, then converted to TensorFlow Lite for mobile deployment through the Flutter framework. Experimental results achieved a mean average precision (mAP50–95) of 0.581, with strong detection performance across most classes. The system successfully operated offline, delivering real-time classification accuracy despite dataset imbalance, particularly in the orange rose class. These findings demonstrate that YOLOv8 can be effectively adapted for mobile horticultural applications, offering practical benefits for flower sorting, crop management, and educational use. Future studies are recommended to expand dataset diversity, enhance environmental testing, and explore cloud-based integration for scalable deployment.
Co-Authors Achmad Fahrezi, Irgy Akbar, Yuma Anwar, Anggit Saepul Barronzoeputra, Gaoeng Qalbun Damayanti, Yulia Heryani, Anissa Pramudyah Hidayat, Aditya Zakaria Ibnu Sarky, Fauzan Maharani, Delia Mutia, Selvi Nabilah, Laila Perwitosari, Faizal Joko Ramadhan, Muhammad Arya Rohmansyah, Fadillah Ali Salfa Dhiyaa Azzizah, Putri Sultan Faqih Fiddin Taufik, M. Endang Tri Wahyudi