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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL INSTEK (Informatika Sains dan Teknologi) Jiko (Jurnal Informatika dan komputer) Conference on Innovation and Application of Science and Technology (CIASTECH) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Kreativitas PKM Jurnal Abdimas Mahakam Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer Systematics Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Buana Ilmu Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Scientific Student Journal for Information, Technology and Science Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Buana Pengabdian Eduvest - Journal of Universal Studies Jurnal Pengabdian Masyarakat Nasional Inovasi Gagasan Abdimas & Kuliah Kerja Nyata
Indra, Jamaludin
Universitas Buana Perjuangan Karawang
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Hidroponik Pintar Menggunakan Fuzzy Logic Berbasis Internet of Things Pada Tanaman Selada Aris Martin Kobar; Jamaludin Indra; Yana Cahyana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6731

Abstract

Changes in land use have become one of the drivers of innovation in modern agriculture, so research and development has been carried out on hydroponic systems. Hydroponic systems require special treatment on several parameters for optimal results and good quality. Manual verification is considered less effective because agents have to do it more often, so there are often delays in monitoring and adding nutrient solutions to the water. Previous researchers have controlled hydroponic systems using various methods. To regulate the nutrition of the hydroponic system in the hydroponic management system using Fuzzy Logic Control based on Electrical Conductivity (EC) used in the fuzzy input is the EC error and determining the duration of turning on the nutrient valve which shows the results that the fuzzy logic control can maintain the EC value range according to plant needs with water depth. In this research, monitoring and managing nutrition, temperature and water level in a hydroponic system is designed using Fuzzy Logic based on the Internet of Things so that monitoring and setting Parts Per Million (PPM) can be done automatically so that you can monitor lettuce plants remotely according to their age. can be viewed via the web application. The test results in this research obtained that the accuracy of the TDS sensor in detecting water TDS values was 97.78%, and the accuracy of the DS18B20 sensor in detecting water temperature conditions was 98.37%. The fuzzy test obtained an error value of 10%.
PEMANFAATAN TEKNOLOGI UNTUK MENDUKUNG PEMBELAJARAN SANTRI PADA PONDOK PESANTREN Kiki Ahmad Baihaqi; Ahmad Fauzi; Jamaludin Indra
Jurnal Pengabdian Masyarakat Nasional Vol 3, No 2 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/pemanas.v3i2.24777

Abstract

Technology has penetrated all fields, the technology in question is learning media and learning support applications. Especially in the world of Islamic boarding schools, which is religion-based education, so it balances religious education and technological education. With the pandemic, everything in education has changed, namely, what was previously only conventional and face-to-face-based, education that can be done in any condition that breaks the barriers of time and space. So training is needed to optimize the use of online-based learning support media. The training results from 30 out of 35 people successfully completed the training stages and the remaining 5 passed with improvements.
Analisis Sentimen Pemboikotan Produk dengan Pendekatan Algoritma Naïve Bayes Media Sosial X Rizky Rifaldi; Jamaludin Indra; Adi Rizky Pratama; Ayu Ratna Juwita
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5420

Abstract

This research aims to analyze sentiment regarding the problem of product boycotting by the public using the Naive Bayes algorithm. 1426 data were collected from social media x to study consumer behavior towards certain products. Through the application of the Naive Bayes algorithm, sentiment analysis was carried out to identify patterns in consumer opinions regarding boycotting the products studied. Experimental results show that the Naive Bayes algorithm succeeded in achieving 81% accuracy in classifying sentiment towards products. This shows the algorithm's ability to analyze consumer sentiment effectively, which can provide valuable insights for companies in understanding public perception and managing the reputation of their products. The practical implication of this research is the importance of utilizing sentiment analysis techniques in marketing strategy and brand management to increase product competitiveness in a competitive market.
PERANCANGAN SISTEM PENGENALAN NOMINAL MATA UANG RUPIAH BERBASIS ANDROID MENGGUNAKAN TENSORFLOW LITE Robinson Nababan; Jamaludin Indra; Ayu Juwita
Scientific Student Journal for Information, Technology and Science Vol. 4 No. 2 (2023): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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

Abstract

Uang kertas merupakan alat pembayaran yang sah yang sudah di ciptakan oleh manusia yang digunakan untuk menggantikan sistem barter. Rupiah adalah mata uang Negara Indonesia yang dikelola dan dikeluarkan oleh Bank Indonesia yang digunakan masyarakat Indonesia sebagai alat transaksi pembayaran yang sah di Indonesia. Saat ini proses transaksi jual beli tidak dilakukan secara langsung antara penjual dan pembeli, perkembang teknologi yang semakin tinggi sudah banyak transaksi jual beli dengan mesin, sehingga dibutuhkan alat yang bisa mendeteksi nominal uang yang lebih akurat yang bisa menggantikan manusia. Berdasarkan masalah dan solusi penelitian sebelumnya, maka penelitian ini bertujuan untuk merancang sistem pengenalan mata uang rupiah dengan mendeteksi nominal di uang kertas dengan menghasilkan audio suara dengan menggunakan tensorflow lite. Hasil pada penelitian ini menggunakan tensorflow lite berhasil mengklasifikasikan pengenalan nominal mata uang rupiah dengan tingkatklasifikasi 90% dari hasil 30 kali pengujian.
Sistem Pakar Pendeteksi Dini Penyakit Jantung Menggunakan Algoritma Certainty Factor Berbasis Android Khoirull Munazzal; Jamaludin Indra; Santi Lestari
Scientific Student Journal for Information, Technology and Science Vol. 5 No. 1 (2024): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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

Abstract

Kurangnya pengetahuan tentang penyakit jantung menjadi penyebab meningkatya penderita penyakit jantung. Seringkali masyarakat tidak menyadari adanya gejala penyakit jantung di dalam tubuhnya. Di samping itu perkembangan teknologi yang pesat terutama di bidang smartphone berbasis android yang kini hampir dimiliki oleh seluruh kalangan masyarakat. Salah satu upaya untuk mengatasi permasalahan tersebut yaitu dengan membuat aplikasi Sistem Pakar untuk Mendeteksi Dini Penyakit Jantung. Algoritma certainty factor digunakan untuk menentukan persentase nilai kepercayaan penyakit berdasarkan gejala fisik yang dirasakan. Proses perhitungan nilai CF dipengaruhi oleh bobot pakar dan bobot pengguna. Sistem diaplikasikan pada smartphone dengan sistem operasi android. Hasilnya sistem dapat mendeteksi tiga jenis penyakit jantung meliputi jantung koroner, aritmia dan gagal jantung serta menampilkan informasi dari masing-masing penyakit. Basisdata MySQL digunakan untuk menyimpan data sistem pakar. Hasil pengujian menggunakan whitebox dan blackbox dapat menilai kinerja fungsi sistem dengan nilai 100% dari 17 skenario pengujian. Hasil pengujian pakar dapat mengetahui akurasi antara sistem dengan diagnosa manual pakar memperoleh nilai 100% dengan 14 kali pengujian.
Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Kecacatan Pada Proses Welding di Perusahaan Manufacturing Saefulloh, Nandang; Indra, Jamaludin; Rahmat, Rahmat; Juwita, Ayu Ratna
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5321

Abstract

Manufacturing industry has become one of the largest sectors in Indonesia, driven by increasing demand from the public. A primary concern to meet both local and international market needs is product quality. In ensuring high-quality standards, production processes require strict quality control. One common issue in quality control is defects occurring during the welding process, which significantly affects inspection cycle times. To address this, the Convolutional Neural Network (CNN) approach with VGG-16 architecture can help classify defects in the welding process. This method not only expedites the defect classification process but also enhances the accuracy of identifying product defects. The stages of implementing this method include dataset preparation, data preprocessing, CNN model design, model training, and performance evaluation. Evaluation results demonstrate that the use of automatic defect detection technology, especially with balanced data scenarios, can significantly improve quality control performance. Accuracy, precision, recall, and F1-score achieve excellent levels, reaching 92%. Thus, this research provides a significant contribution to enhancing production efficiency and improving product quality in the motorcycle manufacturing industry in Indonesia. It is hoped that the use of this technology will assist manufacturing companies in identifying and addressing production defects more effectively, thereby enhancing the overall competitiveness of Indonesia's manufacturing industry.
Implementasi Metode Resampling Dalam Menangani Data Imbalance Pada Klasifikasi Multiclass Penyakit Thyroid Nugraha, Najmi Cahaya; Hikmayanti, Hanny; Indra, Jamaludin; Juwita, Ayu Ratna
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5652

Abstract

It is estimated that at least 17 million Indonesians suffer from thyroid disorders. Interestingly, nearly 60% of those living with a thyroid disorder do not receive a diagnosis. Thus, it is necessary to carry out research that applies methods to predict thyroid disease. Before applying prediction methods, it is crucial to implement classification methods to obtain an accurate prediction model. However, to achieve optimal classification results and to avoid inaccuracies, a balance in the used data is required. Data imbalance is a condition where the ratio between classes in the data is uneven, which can result in the generated model becoming biased. The main objective of the research is to present a solution that can improve the accuracy of early detection of thyroid diseases through addressing data imbalance and implementing appropriate classification algorithms. The research methodology began with the collection and analysis of a dataset consisting of 9172 data points. Preprocessing was then performed, resulting in 5321 training data points and 1331 test data points. The testing phase employed 7 different classification algorithms with 7 different resampling methods and evaluation using a confusion matrix. This research achieved the highest accuracy rate of 98%, obtained from the combination of the Random Forest Algorithm and the Random Over Sampling method. It can be concluded that the combination of the Random Forest Algorithm with the Random Over Sampling resampling method can improve early detection accuracy for thyroid diseases.
Deteksi Jenis Penyakit Pada Tanaman Padi Menggunakan Yolo V5 Muhammad Deden Miftah Fauzi; Tohirin Al Mudzakir; Cici Emilia Sukmawati; Jamaludin Indra
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.2009

Abstract

The problem of disease in rice plants is an obstacle faced by farmers after planting. One of the rice diseases that really worries farmers is disease brown spot, Bacterial Blight, and blast, which causes the leaves to turn yellow prematurely, spots and rice stalks rot. One farmer in Parigi Village whose rice field was attacked by disease suffered a loss of Rp. 6,000,000 to Rp. 8,000,000 per hectare. From all the rice fields of Parigi Village residents. This research aims to detect types of disease in rice plants by applying methods deep learning using YOLO v5 (You only Look Once). The trained model is able to recognize Brown Spot, Bacterial Blight and Blast diseases with a high level of accuracy.  In this analysis, two epochs stand out as the best candidates, namely epoch 250 and epoch 200. At epoch 250, the model shows the highest precision (0.802) and a strong mAP@0.5 value (0.702), indicating excellent model performance without overfitting. Meanwhile, at epoch 200, although precision and recall were slightly lower, the highest mAP@0.5:0.95 value (0.393) indicated better generalization ability. Based on these metrics, epoch 150 is identified as the optimal epoch, although epoch 200 also shows strong performance, especially in generalization over a wide range of threshold IoU. The calculation results show the following performance metrics: Precision: 92.5%; Recall: 90.8%; F1-Score: 91.6%; Mean Average Precision (mAP): 93.2%.
Sosialisasi Alur Kerja Sistem Electronic Traffic Law Enforcement (ETLE) Dari Segi Ilmu Komputer Vision Pada Masyarakat Baihaqi, Kiki Ahmad; Fauzi, Ahmad; Indra, Jamaludin
Jurnal KKN Kuliah Kerja Nyata Pengabdian Masyarakat Vol. 1 No. 3 (2024): Jurnal Inovasi Gagasan Abdimas dan Kuliah Kerja Nyata
Publisher : IGAKERTA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70234/1km1j504

Abstract

Sistem deteksi kendaraan bermotor yang digunakan pada sistem E-TLE bukan hal baru, dikarenakan sistem tersebut sudah banyak diteliti dan diimplementasikan dinegara maju. Deteksi yang dimaksud adalah mendeteksi nomor polisi yang ada pada suatu kendaraan kemudian dikonversi menjadi laporan dan dikategorikan dalam bentuk pelanggaran seperti apa sesuai perundang-undangan. Namu, tidak banyak Masyarakat yang paham akan alur proses deteksi dan akurasi dari sistem tersebut. Sehingga dibutuhkan sosialisasi kepada Masyarakat lebih lanjut dan mendalam. Sosialisasi dilakukan pada kegiatan pengabdian kepada masyakarat yang dilakukan ditingkat desa yang berada dibagian luar Kabupaten Karawang. Masyarakat yang mengikuti adalah perangkat desa setingkat RT dikarenakan nanti akan disosialisasikan kembali oleh aparatur desa kepada Masyarakat. Peserta berjumlah 30 orang yang dibagi menjadi 2 termin pagi dan sore, dikarenakan kapasitas ruangan dan faktor efektifitas penjelasan agar kondusif. Hasilnya 50%  yaitu berjumlah 15 orang tidak mengetahui perihal sistem seperti ini bisa akurat dan mengidentifikasikan nomor kendaraan serta pelaggarannya
PERSONAL PROTCTIVE EQUIPMENT DETECTION FOR OCCUPATIONAL SAFETY AND HEALTH USING YOLOV8 IN MANUFACTURING COMPANIES: DETEKSI ALAT PELINDUNG DIRI (APD) UNTUK KESELAMATAN DAN KESEHATAN KERJA MENGGUNAKAN YOLOV8 Gapur, Abdul; Wahiddin, Deden; Mudzakir, Tohirin Al; Indra, Jamaludin
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2619

Abstract

According to data from BPJS Keltelnagakelrjaan, 265,333 cases of work accidents were recorded in 2022. The use of personal protective equipment (PPE) is very important in reducing and preventing work accidents in the company. Although PPE cannot eliminate all risks, it is possible to minimise the number of work accidents in manufacturing companies. The aim of this research is to automatically select Personal Protective Equipment (PPE) in the form of hard hats and vests and to improve the accuracy results using the YOLOv8 model. With a dataset of 500 helmet and velst images for deltelksi which will be categorised into 4 classes namely hellelm, velst, no-hellelm, no-velst. The dataset used is 500 data, which is then divided into three datasets, namely: training data as much as 70%, validation data as much as 20%, and telst data as much as 10%, from the dataselt telrselbut the best results of testing data values from 50 dataselt the accuracy results obtained are 0.98. It is hoped that with the use of Meltode and accuracy results using Yolo v8, it can be used in companies by detecting Personal Protective Equipment (PPE) with fast and accurate results, so that it can be applied in monitoring the use of PPE in manufacturing companies to reduce the risk of work accidents in manufacturing companies
Co-Authors AA Sudharmawan, AA Abdul Gapur Achmad, Syifa Latifah Adi Rizky Pratama Adi Rizky Pratama Agung Susilo Yuda Irawan Ahmad Afifur Rahman Ahmad Fauzi Ahmad Fauzi Ahmad Rahman Al Fathir Rizal Januar Alif Kirana Anton Romadoni Junior Apriade Voutama April Hananto Ardiansyah, Fikri Arif Nurman Arip Solehudin Aris Martin Kobar Arum Puspita Lestari, Santi Asep Jamaludin Aviv Yuniar Rahman Awal, Elsa Elvira Ayu Juwita Ayu Ratna Juwita Azis Saputra Azzahra, Wava Lativa Baihaqi, Kiki Ahmad Cici Emilia Sukmawati Dadang Yusup Deden Wahiddin Deny Maulana Dwi Sulistya Kusumaningrum Dwi Vina Wijaya Eko Pramono Fadmadika, Fadilla Faisal, Sutan Fauzi Ahmad Muda Fauzi, Ahmad Firdaus, Thoriq Janati Firmansyah Maulana Fitri Nur Masruriyah, Anis Garno . Garno, Garno Gugy Guztaman Munzi Hanny Hikmayanti Handayani Hanung Pangestu Rahman Hilda Fitriana Dewi Hilda Novita Hilda Yulia Novita Irma Putri Rahayu Juwita, Ayu Ratna Karyanto, Dony Dwi Khoirull Munazzal Kusumaningrum, Dwi Sulistya Lestari, Santi Arum Puspita M Andrian Agustyan Maharina, Maharina Maliah Andriyani Mudzakir, Tohirin Al Muhammad Cesar Afriansyah Arief Muhammad Deden Miftah Fauzi Muhammad Imam Naufal Muhammad Khoiruddin Harahap Muhammad Raja Nurhusen Muhammad Romadhon Nazori AZ Novalia, Elfina Nugraha, Najmi Cahaya Nurdin, Cherry Januar Nurlaelasari, Euis Nursyawalni, Reva Paryono, Tukino Pratama, Adi Rizky Purnama, Ariya Purnomo, Indarto Aditya Rahmat Hidayat Rahmat Rahmat Rahmat Rahmat Rija Nur Hijriyya Rissa Ilmia Agustin Rizki, Lutfi Trisandi Rizky Rifaldi Robinson Nababan Rohana, Tatang Romlah Saefulloh, Nandang Sandi Susanto Santi Lestari Sihabudin Sihabudin, Sihabudin Siregar, Amril Mutoi Siti Robiah Suparno Sutan Faisal Syahrul Azis Tatang Rohana Tia Astiyah Hasan Tohirin Al Mudzakir Tohirin Mudzakir Toif Muhayat Tri Vicika, Vikha Ulfa Amelia Wahiddin, Deden Wildan Amin Wiharja Yana Cahyana Yogi Firman Alfiansyah