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PEMBANGKIT PULSA ARUS SEBAGAI SUMBER ARUS DENGAN METODA ISI-BUANG MUATAN KAPASITOR UNTUK PEMBUATAN MAGNET Djati Handoko; Hasan Fahad; Budhy Kurniawan; Azwar Manaf
Jurnal Sains Materi Indonesia Vol 3, No 2: FEBRUARI 2002
Publisher : Center for Science & Technology of Advanced Materials - National Nuclear Energy Agency

PEMBANGKIT PULSA ARUS SEBAGAI SUMBER ARUS DENGAN METODA ISI-BUANG MUATAN KAPASITOR UNTUK PEMBUATAN MAGNET. Telah dibuat dan dianalisis sebuah instrumen pembangkit arus yang besar dengan metoda pengisian dan pembuangan muatan pada kapasitor. Arus listrik yang dihasilkan berbentuk pulsa dengan besar 0,25 kA. Rangkaian yang dibuat seperti terlihat pada gambar menggunakan 2 buah kapasitor dengan kapasitansi 3300 mF/350V yang dihubungkan secara paralel. Proses pengisian dan pembuangan muatan dilakukan dengan membuka/menutup saklar (menggunakan SCR) yang dapat diatur waktunya menggunakan timer. Analisis dilakukan terhadap perubahan lama waktu pengisian terhadap besar tegangan kapsitor yang dihasilkan. Pada kasus ini timer dan SCR sangat memegang peranan. Ternyata waktu yang dibutuhkan untuk mencapai harga tegangan maksimum berada pada orde milidetik. Untuk membuktikan keberhasilan instrumen ini dilakukan pengujian terhadap bahan magnet yang berbasis Nd-Fe-B dan SmCo. Magnet yang diperoleh memiliki intensitas medan magnet yang lebih besar bila dibandingkan dengan yang menggunakan sistem elektromagnet biasa.

Optimasi Hyperparameter pada Model XGBoost untuk Estimasi Curah Hujan: Studi Kasus Kota Pontianak Auriwan Yasper; Djati Handoko; Maulana Putra; Harry Kasuma Aliwarga; Mohammad Syamsu Rosid Rosid
Jurnal Penelitian Pendidikan IPA Vol. 9 No. 9 (2023): September
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i9.3890

Estimating rainfall accurately is crucial for both the community and various institutions involved in managing water resources and preventing disasters. The XGBoost model has demonstrated its effectiveness in predicting rainfall, but it still requires fine-tuning of hyperparameters to enhance its performance. This study seeks to determine the optimal learning rate for rainfall prediction while keeping the max_depth and n_estimator parameters fixed. The hyperparameter optimization process was carried out using a two-step approach: an initial coarse search using RandomizedSearchCV followed by a more detailed fine-tuning using GridSearchCV. The model's foundation relied on historical rainfall data gathered over three months from the Automated Weather Observed System (AWOS) at the Pontianak Meteorological Station, recorded on an hourly basis. To assess the model's performance, several metrics were employed, including accuracy, precision, recall, F1 score, and ROC-AUC. The model demonstrated promising results, with accuracy, precision, recall, and F1 score all reaching 95%, indicating its ability to effectively predict rainfall. However, the ROC-AUC score was somewhat lower at 62%. After conducting the hyperparameter search, the optimal learning rate determined for the model, utilizing the 2040 dataset, was found to be 0.204.

Implementation of Rainfall Monitoring through an Information System Based on Radar and Satellite Image Data using the Kalman Filter Method Sugiarto; Putra, Maulana; Syaefudin, Mohamad Anwar; Rosid , Mohammad Syamsu; Handoko, Djati
Journal of Information Technology and Computer Science Vol. 9 No. 2: August 2024
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.92620

Rainfall information is important for water resource management and disaster mitigation, so a monitoring system is needed that can provide spatial information. A rainfall monitoring information system uses radar and satellite images based on the Kalman Filter method is a website information system that aims to make it easier for users to monitor rainfall from a latitude and longitude coordinate point. The rain intensity data is in pixel colours (Red, Green, Blue) from the MAX (dBZ) product image of the EEC Weather Radar in Lampung and cloud top temperature data from the Himawari satellite. The image pixel colour data is then processed using the Kalman filter and displayed on the integrated website. The website's appearance is designed to be interactive to accommodate user customization, and the data displayed has been adapted to geographic information systems. Implementing a rainfall information system based on weather radar and satellite imagery disseminated through the website is expected to help users, both the general public and stakeholders, access real-time weather information, especially rainfall.

Comparison of YOLOv3-tiny and YOLOv4-tiny in the Implementation Handgun, Shotgun, and Rifle Detection Using Raspberry Pi 4B S. Hi. Rauf, Faris zulkarnain; Handoko, Djati; Pradana, Ilham S; Alifta, Dimas
Jurnal Elektronika dan Telekomunikasi Vol 24, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.602

Criminal activities frequently involve carryable weapons such as handguns, shotguns, and rifle classes. Frequently, the targets of these weapons that are captured are concealed from plain sight by the people of the crowd. The detection process for these weapons can be assisted by using deep learning. In this case, we intend to identify the model of the firearm that was detected. This research aims to apply one of the deep learning concepts, namely You Only Look Once (YOLO). The authors use versions of YOLOv3-tiny and Yolov4-tiny for the detection and classification of types of weapons, which are one of the fastest and most accurate methods of object detection, outperforming other detection algorithms. However, both require heavy computer architecture. Therefore, YOLOv3-tiny and YOLOv4-tiny, lighter versions of YOLOv3, can be solutions for smaller architectures. YOLOv3-tiny and YOLOv4-tiny have higher FPS, which is supposed to yield faster performance. Since YOLOv3-tiny and YOLOv4-tiny are modified versions of YOLOv3, the accuracy is improved, and YOLOv3 is already outperforming Faster Single Shot Detector (SSD) and Faster Region with Convolutional Neural Network (R-CNN). The authors employ YOLOv3-tiny and YOLOv4-tiny due to the fact that the Frame Per Second (FPS) and Mean Average Precision (mAP) performance of both approaches are superior in object detection. The study found that YOLOv3-tiny had a high FPS and low mAP performance: an average Intersection over Union (IoU) score of 71.54%, an accuracy of 90%, a recall score of 78%, an F1 score of 84%, and an mAP of 86.7%. While YOLOv4-tiny has low FPS and high mAP: an average IoU score of 73.19%, an accuracy of 90%, a recall score of 84%, an F1 score of 87%, and an mAP of 90.7%.

Weld Defect Detection and Classification based on Deep Learning Method: A Review Tito Wahyu Purnomo; Finkan Danitasari; Djati Handoko
Jurnal Ilmu Komputer dan Informasi Vol. 16 No. 1 (2023): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

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

The inspection of weld defects utilizing nondestructive testing techniques based on radiography is essential for ensuring the operability and safety of weld joints in metals or other materials. During the process of welding, weld defects such as cracks, cavity or porosity, lack of penetration, slag inclusion, and metallic inclusion may occur. Due to the limitations of manual interpretation and evaluation, recent research has focused on the automation of weld defect detection and classification from radiographic images. The application of deep learning algorithms enables automated inspection. The deep learning architectures for building weld defect classification models were discussed. This paper concludes with a discussion of the achievements of automation methods and a presentation of the research recommendations for the future.

A Comparison of CNN-based Image Feature Extractors for Weld Defects Classification Tito Wahyu Purnomo; Harun Al Rasyid Ramadhany; Hapsara Hadi Carita Jati; Djati Handoko
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 14, No 1 (2024): April
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v14i1.72509

Classification of the types of weld defects is one of the stages of evaluating radiographic images, which is an essential step in controlling the quality of welded joints in materials. By automating the weld defects classification based on deep learning and the CNN architecture, it is possible to overcome the limitations of visually or manually evaluating radiographic images. Good accuracy in classification models for weld defects requires the availability of sufficient datasets. In reality, however, the radiographic image dataset accessible to the public is limited and imbalanced between classes. Consequently, simple image cropping and augmentation techniques are implemented during the data preparation stage. To construct a weld defect classification model, we proposed to utilize the transfer learning method by employing a pre-trained CNN architecture as a feature extractor, including DenseNet201, InceptionV3, MobileNetV2, NASNetMobile, ResNet50V2, VGG16, VGG19, and Xception, which are linked to a simple classification model based on multilayer perceptron. The test results indicate that the three best classification models were obtained by using the DenseNet201 feature extractor with a test accuracy value of 100%, followed by ResNet50V2 and InceptionV3 with an accuracy of 99.17%. These outcomes are better compared to state-of-the-art classification models with a maximum of six classes of defects. The research findings may assist radiography experts in evaluating radiographic images more accurately and efficiently.

Low-cost portable potentiostat for real-time insulin concentration estimation based on electrochemical sensors Dewi, Fitria Yunita; Aliwarga, Harry Kusuma; Handoko, Djati
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3683-3695

Administering incorrect insulin dosages to diabetic patients can be fatal, leading to severe health consequences. Insulin detection, in conjunction with blood glucose monitoring, can significantly enhance diagnostic accuracy. Electrochemical methods for insulin detection offer a low-cost and portable solution. This study presents an insulin concentration estimation system using a customized electrochemical potentiostat operating in real-time via Bluetooth low energy (BLE). Conventional electrochemical sensing, which relies on calibration curves to determine concentration, poses accuracy limitations in portable devices. To address this, we implement a multiple- predictor approach that incorporates peak currents from multiple cycles of cyclic voltammetry responses and the electroactive surface area of a multi- walled carbon nanotube (MWCNT-COOH) modified screen-printed sensor. This modified sensor enhances sensitivity compared to bare screen-printed carbon sensors, making it suitable for low-volume and portable applications. Through cross-validation, our method demonstrated strong performance, achieving a determination coefficient (R²) greater than 0.90 for all training dataset combinations and greater than 0.85 for all testing dataset combinations. Hypothesis testing further confirmed the statistical significance of the electroactive surface area (p=0.006) as predictor, indicating its meaningful contribution to concentration estimation. This approach improves portable detection performance, supporting the development of affordable and reliable personal insulin monitoring systems.

NON-INVASIVE DETERMINATION OF LIQUID DIFFUSION COEFFICIENTS USING LASER BEAM DEFLECTION AND REFRACTIVE INDEX GRADIENTS: A STUDY ON NaCl Handoko, Djati; Hifzhi, Affan; Sudarmaji, Arief; Dewi, Fitria Yunita
Indonesian Physical Review Vol. 8 No. 3 (2025)
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ipr.v8i3.500

This research describes and verifies the creation of a straightforward system for determining liquid diffusion coefficients using the Laser Beam Deflection (LBD) technique for measuring diffusion rates in sodium chloride solutions. The system exploits refractive index gradients that develop during diffusion to produce a detectable laser beam deviation, which is subsequently analyzed to calculate diffusion coefficients. Our experimental setup, which builds on Wiener's original design with several improvements, consists of a laser source with a cylindrical lens, a diffusion cell, and a screen for capturing projected images. We conducted an in-depth analysis of time-dependent measurements (5, 20, and 45 minutes), concentration variations (20/100, 25/100, and 30/100 NaCl/aquades ratios), and geometric configurations (30°, 45°, and 60° tilt angles) and found that the initial diffusion coefficients exhibit time-dependent behavior before stabilizing at approximately. Within the examined range, concentration had a negligible impact on diffusion coefficients, but the geometric orientation had a substantial effect on measurement accuracy, resulting in a measurement error of approximately 3.00% when the configuration was set at 45°. Linear correlations between the natural logarithm of the ratio of the concentration difference to time, and the inverse square of the height, were found to be consistent with Fick's second law of diffusion under all tested conditions. This non-invasive approach offers a dependable substitute for conventional methods of diffusion measurement, which may be utilised in fields such as solution chemistry, food science, and pharmaceutical formulations.

DYNAMICS NOISE BEHAVIORS ON MAGNETO-OPTICAL KERR EFFECT MEASUREMENT SYSTEM Handoko, Djati; Kim, Dong-Hyun
Indonesian Physical Review Vol. 6 No. 2 (2023)
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ipr.v6i2.231

Nowadays, computer and data processing industry are moving to nanomagnetic devices technology. One of the common measurement systems to observe nanomagnetic device are magneto optical Kerr effect and Faraday effects. The magneto-optical Kerr effect measurement system has been fabricated and precision noise measurement configuration was observed. A light intensity, which was reflected by thin film nanomagnetic surface, was measured accompany with its noise level. The lock-in amplifier was attached to pick up hysteresis signal and low noise level. Different frequency of lock-in amplifier was carried out to observe dynamics noise level behavior. Interestingly, we found butterfly shape noise corresponding to hysteresis loop shape. Furthermore, Â noise behavior with 0.94 scaling exponent, was found with respect to lock-in amplifier frequencies suggested that measuring in low frequency became more challenging.

Optimasi Hyperparameter pada Model XGBoost untuk Estimasi Curah Hujan: Studi Kasus Kota Pontianak Yasper, Auriwan; Handoko, Djati; Putra, Maulana; Aliwarga, Harry Kasuma; Rosid, Mohammad Syamsu Rosid
Jurnal Penelitian Pendidikan IPA Vol 9 No 9 (2023): September
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i9.3890

Estimating rainfall accurately is crucial for both the community and various institutions involved in managing water resources and preventing disasters. The XGBoost model has demonstrated its effectiveness in predicting rainfall, but it still requires fine-tuning of hyperparameters to enhance its performance. This study seeks to determine the optimal learning rate for rainfall prediction while keeping the max_depth and n_estimator parameters fixed. The hyperparameter optimization process was carried out using a two-step approach: an initial coarse search using RandomizedSearchCV followed by a more detailed fine-tuning using GridSearchCV. The model's foundation relied on historical rainfall data gathered over three months from the Automated Weather Observed System (AWOS) at the Pontianak Meteorological Station, recorded on an hourly basis. To assess the model's performance, several metrics were employed, including accuracy, precision, recall, F1 score, and ROC-AUC. The model demonstrated promising results, with accuracy, precision, recall, and F1 score all reaching 95%, indicating its ability to effectively predict rainfall. However, the ROC-AUC score was somewhat lower at 62%. After conducting the hyperparameter search, the optimal learning rate determined for the model, utilizing the 2040 dataset, was found to be 0.204.

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