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All Journal JOIN (Jurnal Online Informatika) Jurnal Pengembangan Riset dan Observasi Teknik Informatika Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Dharmabakti Nagri International Journal of Information Technology and Computer Science Applications (IJITCSA) Prosiding Seminar Nasional Unimus Jurnal Riset Informatika dan Teknologi Informasi (JRITI)
Tb Ai Munandar
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Health Professions Nursing Public Health Social Sciences Other

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Rancangan Prototype Pendukung Keputusan Penentuan Calon Penerima Bantuan Pangan Non Tunai Di Desa Tenjoayu Menggunakan Metode Weighted Product Suhaedi; Tb Ai Munandar; Haris Triono Sigit
ProTekInfo(Pengembangan Riset dan Observasi Teknik Informatika) Vol. 8 No. 1 (2021)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/protekinfo.v8i1.5018

Abstract

Penentuan Penerima bantuan merupakan salah satu masalah yang menjadi perhatian serius pemerintah desa Tenjoayu, karena banyaknya data pengusulan bantuan yang masuk tentunya sangat merepotkan bagi pemerintah desa dalam menentukan penerima bantuan. Oleh karena itu, dibutuhkan suatu sistem yang mampu membantu dalam menentukan penerima bantuan. Sistem ini mengimplementasikan metode weighted product, karena metode ini memberikan nilai bobot pada setiap kriteria dan selanjutnya dilakukan perangkingan. Dalam penelitian ini parameter yang digunakan yaitu penghasilan, kondisi rumah, mata pencaharian dan anak. Sistem ini dibangun dengan bahasa pemrograman WEB, dimana hasil output sistem ini berupa perangkingan calon penerima bantuan sesuai dengan hasil yang diperoleh. Dengan dibuatnya Sistem Pendukung Keputusan Penentuan Penerima Bantuan Pangan Non Tunai diharapkan mampu mempermudah dalam penentuan penerima bantuan di desa Tenjoayu.
Implementasi Augmented Reality untuk Pengembangan Aplikasi Pengenalan Senjata Tradisional Kujang Muhammad Azhar Khairi; TB Ai Munandar; Siti Setiawati
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 2 No 2 (2022): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v2i2.704

Abstract

Kujang merupakan salah satu senjata tradisional yang menjadi ciri khas Jawa Barat. Namun masih banyak masyarakat yang belum mengetahui makna dan jenis-jenis dari kujang. Untuk mengenal kujang saat ini sangat sulit, dikarenakan sedikitnya masyarakat yang mempunyai atau mengkoleksi kujang. Dan juga tidak semua museum mempunyai jenis-jenis kujang yang lengkap, seperti Museum Pusaka Taman Mini Indonesia Indah (TMII). Museum tersebut hanya memiliki 4 jenis kujang yang dapat diperkenalkan. Informasi yang diberikan kepada pengujung juga tidak terlalu banyak. Penelitian ini bertujuan untuk mengembangkan aplikasi pengenalan senjata tradisional kujang menggunakan augmented reality, sehingga memudahkan pengunjung Museum Pusaka Taman Mini Indonesia Indah (TMII) untuk mengenal kujang dengan melihat bentuk secara 3D serta menambahkan materi mengenai kujang. Penggunaan aplikasi augmented reality ini menggunakan marker yang nantinya akan dideteksi oleh kamera dan memunculkan objek 3D. Pembuatan aplikasi augmented reality ini menggunakan tools vuforia. Hasil penelitian memperlihatkan bahwa aplikasi yang dikembangkan sangat diterima dengan baik oleh pengguna. Hal ini diperlihatkan dengan nilai system usability scale dari 58 responden yang menghasilkan skor rata-rata SUS sebesar 82,97. Skor tersebut termasuk ke dalam grade scale B, adjective rating good, dan acceptable ranges high. Kata kunci: kujang, augmented reality, marker, vuforia.
Program Pendampingan Implementasi E-Learning System Untuk Peningkatan Pembelajaran Daring Era Pandemi Covid-19 Bagi Guru SD IT Al Muhajirin Kota Cilegon Tb Ai Munandar; Harsiti Harsiti; Tb Sofwan Hadi
Prosiding Seminar Nasional Unimus Vol 4 (2021): Inovasi Riset dan Pengabdian Masyarakat Post Pandemi Covid-19 Menuju Indonesia Tangguh
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Era pandemi COVID-19 yang melanda dunia termasuk Indonesia telah mengubah cara kerjadiberbagai segi kehidupan, termasuk model pembelajaran. Efek pandemi telah mengubah carapandang pembelajaran yang awalnya mengedepankan kegiatan tatap muka secara utuh, kini harusdipaksa melaksanakan kegiatan belajar mengajar menggunakan teknologi informasi. Pengabdiankepada masyarakat ini bertujuan untuk menginisiasi, memperkenalkan dan membangun sistempembelajaran daring menggunakan platform learning management system berbasis open source bagiguru dan perangkat sekolah di SD IT Al Muhajirin. Untuk mencapai tujuan tersebut dilakukanpelatihan, workshop, diskusi kebutuhan dan pendamping pemanfaatan, penggunaan sistem e-learningbaik dari sisi guru maupun staff admin yang bertugas mengelola sistem daring nantinya. Dengandilaksanakannya kegiatan PKM ada perubahan kenaikan tingkat pemahaman pemanfaatan e-learningsystem untuk membantu proses belajar mengajar khususnya secara daring. Materi yang disampaikanselama kegiatan PKM juga mendapatkan respon yang sangat baik bahkan sebagai besar pesertamenyatakan bahwa kegiatan yang dilakukan perlu untuk dilanjutkan dimasa mendatang agar mitraPKM semakin memiliki pemahaman yang baik dan kuat untuk impementasi e-learning secaramenyeluruh. Kata Kunci : pembelajaran, e-learning, pandemi, covid-19, open source.
Sentiment Analysis of the Use of Digital Banking Service Applications On Google Play Store Reviews Using Naïve Bayes Method Amalia Nur Soliha; Tb Ai Munandar; Muhammad Yasir
International Journal of Information Technology and Computer Science Applications Vol. 1 No. 3 (2023): September - December 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v1i3.40

Abstract

The development of the financial system is characterized by the emergence of digital banking service applications that are widely circulated and can be accessed for free. With so many applications, users often feel confused in choosing which applications are safe to use. Before downloading an application on the Google Play Store, users will usually look at ratings and reviews first. However, the title of the best application cannot be pinned if only seen from the rating and number of downloads. This research was conducted to analyze sentiment on user reviews of digital banking service applications on the Google Play Store using the NBC (Naïve Bayes Classifier) method. Research using the NBC algorithm produced an accuracy value of 81% on the classification of Allo Bank reviews and 78% on the classification of Line Bank reviews
Workshop Peningkatan Pemahaman Profesi Software Engineer Bagi Calon Entrepreneur Muda Mahasiswa Semester Akhir FASILKOM UBHARA JAYA Handayani, Dwipa; Tb Ai Munandar; Retno Wulandari
Jurnal Dharmabakti Nagri Vol 2 No 1 (2023): Desember 2023 - Maret 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jdn.v2i1.117

Abstract

Workshop Peningkatan Pemahaman Profesi Software Engineer Bagi Calon Entrepreneur Muda Mahasiswa Semester Akhir FASILKOM UBHARA JAYA merupakan Pengabdian kepada Masyarakat (PKM) yang bertujuan untuk meningkatkan pemahaman mahasiswa terhadap profesi software engineer dengan fokus pada kesiapan menjadi calon entrepreneur muda. Workshop ini dilaksanakan secara daring melalui platform Zoom Meeting dengan melibatkan 40 mahasiswa semester akhir FASILKOM UBHARA JAYA. Materi workshop dirancang untuk memberikan pemahaman holistik tentang peran software engineer dalam industri teknologi informasi yang terus berkembang. Workshop dilaksanakan dalam tiga sesi utama: pengantar profesi software engineer, keterampilan dan pengetahuan yang diperlukan, serta kesiapan menjadi entrepreneur muda. Sesi praktikum yang diselenggarakan memberikan peserta pengalaman langsung dalam menerapkan konsep-konsep yang telah dipelajari. Hasil pelaksanaan workshop menunjukkan peningkatan pemahaman peserta terhadap profesi software engineer dan kewirausahaan. Tanggapan positif peserta mencerminkan daya tarik materi dan metode penyampaian, serta identifikasi peningkatan minat terhadap kewirausahaan di antara peserta. Meskipun beberapa peserta menghadapi kendala teknis, respons cepat dan panduan tambahan dari tim pelaksana memastikan kelancaran workshop. Hasil workshop dapat memberikan manfaat lebih luas kepada masyarakat akademis dan industri. Kata Kunci: Workshop, Profesi Software Engineer, Kewirausahaan, Peningkatan Pemahaman, Mahasiswa, Teknologi Informasi
Data Mining for Heart Disease Prediction Based on Echocardiogram and Electrocardiogram Data Tb Ai Munandar
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1027

Abstract

Traditional methods of detecting cardiac illness are often problematic in the medical field. The doctor must next study and interpret the findings of the patient's medical record received from the electrocardiogram and echocardiogram. These tasks often take a long time and require patience. The use of computational technology in medicine, especially the study of cardiac disease, is not new. Scientists are continuously striving for the most reliable method of diagnosing a patient's cardiac illness, particularly when an integrated system is constructed. The study attempted to propose an alternative for identifying cardiac illness using a supervised learning technique, namely the multi-layer perceptron (MLP). The study started with the collection of patient medical record data, which yielded up to 534 data points, followed by pre-processing and transformation to provide up to 324 data points suitable to be employed by learning algorithms. The last step is to create a heart disease classification model with distinct activation functions using MLP. The degree of classification accuracy, k-fold cross-validation, and bootstrap are all used to test the model. According to the findings of the study, MLP with the Tanh activation function is a more accurate prediction model than logistics and Relu. The classification accuracy level (CA) for MLP with Tanh and k-fold cross-validation is 0.788 in a data-sharing situation, while it is 0.672 with Bootstrap. MLP using the Tanh activation function is the best model based on the CA level and the AUC value, with values of 0.832 (k-fold cross-validation) and 0.857 (bootstrap).
Deteksi Status Internal Battery UPS Berdasarkan Hasil Pengukuran Resistansi Dari Battery Tester Menggunakan Algoritma C4.5 Kusumah, Muhammad Assegaf Raja; Setyawati, Ananda; Wardana, Muhammad Bisma Arya; Tb Ai Munandar; Ajif Yunizar Pratama Yusuf
Jurnal Riset Informatika dan Teknologi Informasi Vol 2 No 1 (2024): Agustus - November 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v2i1.44

Abstract

The purpose of this research is to propose a classification model using the decision tree method that can detect the internal status of UPS batteries by using resistance measurements from a battery tester. Resistance data of UPS batteries were collected from measurements conducted under various conditions. This study focuses on supervised learning models, where the data is processed to form a decision tree using the C4.5 classification algorithm. The test results show that the classification of UPS battery internal status can be achieved with high accuracy. The data presentation results were obtained from 100 units of battery systems in UPS, with two conditions identified: 38 units of batteries with a Normal condition and a presentation data accuracy rate of 100%, and 62 units of batteries with a Fault condition and a presentation data accuracy rate of 100%. By using this classification model, users can monitor the performance of the internal battery in the UPS system and take necessary actions to maintain stable UPS system operation and usage.
Implementasi Metode Arima Data Warehouse Untuk Prediksi Permintaan Suku Cadang Hendrik Hidayatullah; Fitri Sukaesih; Yanuar Arif Hizbulloh; Tb Ai Munandar
Jurnal Riset Informatika dan Teknologi Informasi Vol 1 No 1 (2023): Agustus - November 2023
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v1i1.48

Abstract

Food production company is a company that focuses on the production of instant noodles, and machine reliability is crucial in the production process. To maintain machine reliability, regular maintenance is necessary, and the availability of spare parts is also a key factor in reliability planning. Therefore, spare part management is crucial in the company as it can affect the spare part control system and vice versa. Poor spare part management planning can result in fluctuations in demand for goods. Uncertainty forces the company to determine the minimum and maximum spare parts inventory to be managed. Lack of standards during spare part deliveries leads to excess spare parts. Excess spare parts cause inventory to accumulate in the workshop. However, if there is a shortage of spare parts, it makes maintenance difficult in the production department. Based on the data used, this research is classified as quantitative research that produces numbers. The aim of this research is to predict the demand for spare parts for maintenance processes using the ARIMA (Autoregressive Integrated Moving Average) method. This research is carried out because the proper and effective use of spare parts is essential in maintaining machine and industrial equipment reliability. The ARIMA method is used to identify patterns in spare part demand data and make accurate predictions for future demand. Spare part demand data for a certain period of time is collected and analyzed using statistical software. The results of the research show that the ARIMA method can be used to predict spare part demand with a high level of accuracy. With this prediction, the company can better plan to meet demand and optimize spare part inventory management. The results of this research can provide benefits to the company in improving their operational efficiency and effectiveness while reducing costs related to spare part inventory shortages.
Comparative Analysis of K-Means and Hierarchical Clustering for Regional Welfare Disparity Identification in West Java Province Muhamad Dani Yusuf; Tb Ai Munandar; Khairunnisa Fadhilla Ramdhania
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 3 (2025): September - December 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i3.213

Abstract

This study aims to cluster regencies/cities in West Java Province based on public welfare indicators using the K-Means Clustering and Hierarchical Clustering methods. The data used includes health, economic, population density, and average length of schooling indicators in 2023. Cluster quality evaluation was performed using the silhouette score. The results show that K-Means Clustering with five clusters yields the highest silhouette score of 0.219. For comparison, Hierarchical Clustering with the Ward Linkage method and eight clusters was chosen, having a silhouette score of 0.202, which is the largest among other Hierarchical Clustering methods. The identification of each cluster's characteristics in K-Means reveals areas with multidimensional challenges (Cluster 1), industrial areas with unemployment issues (Cluster 2), areas with high stunting prevalence despite good access to basic facilities (Cluster 3), densely populated urban areas with good welfare but high unemployment (Cluster 4), and areas with very high health complaints and low welfare (Cluster 5). K-Means clusters (except Cluster 4) tend to have a low average length of schooling, below 12 years. Consistency in cluster patterns was found between K-Means and Ward Linkage, especially in advanced urban areas and areas with multidimensional welfare challenges in southern West Java. These findings are expected to serve as a reference for the government and policymakers in formulating more targeted and effective development strategies.
Co-Authors Ajif Yunizar Pratama Yusuf Amalia Nur Soliha Dwipa Handayani Fitri Sukaesih Haris Triono Sigit Harsiti Harsiti Hendrik Hidayatullah Khairunnisa Fadhilla Ramdhania Kusumah, Muhammad Assegaf Raja Muhamad Dani Yusuf Muhammad Azhar Khairi Muhammad Yasir Retno Wulandari Setyawati, Ananda SITI SETIAWATI Suhaedi Tb Sofwan Hadi Wardana, Muhammad Bisma Arya Yanuar Arif Hizbulloh