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All Journal Prosiding Semnastek Jurnal Ilmiah FIFO Jurnal ULTIMA InfoSys TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri Jurnal E-Komtek Indonesian Journal of Electrical Engineering and Computer Science JURPIKAT (Jurnal Pengabdian Kepada Masyarakat)
Herman Bedi Agtriadi, Herman Bedi
Institut Teknologi PLN
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences

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ANALISIS SISTEM PENENTUAN LOKASI GANGGUAN JARINGAN DISTRIBUSI LISTRIK TERINTEGRASI GOOGLE MAP Abdul Haris; Herman Bedi Agtriadi
Jurnal Ilmiah FIFO Vol 9, No 1 (2017)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2017.v9i1.001

Abstract

Tingginya kebutuhan akan listrik dalam kehidupan masyarakat sadar atau tanpa kita sadari kita sering mengalami masalah listrik padam yang diakibatkan kebakaran gardu, pohon tumbang, kebakaran rumah dan beberapa masalah yang dapat mengakibatkan gangguan jaringan distribusi listrik.. Oleh sebab itu dibutuhkan sebuah sistem untuk dapat secara cepat mengetahui gangguan-gangguan yang terjadi dilapangan dan sistem tersebut dapat memberikan petunjuk jalur alternatif yang secara cepat dapat dilalui dan mengetahui titik lokasi terjadi. Teknik yang digunakan dalam penelitian ini adalah memanfaatkan google API, teknologi ini mampu memetakan lokasi terdekat dengan membaca kordinat selain itu teknologi ini mampu menjadi penghubung antara teknologi internet yang berbasis komputer dengan mobile.  Web merupakan aplikasi yang sangat populer dan dapat menjadi media yang dapat digunakan untuk menyediakan informasi secara cepat dimanapun lokasinya sedangkan google map merupakan sarana yang menjadi penunjuk untuk memberikan informasi jalur cepat yang dapat dilalui untuk mencapai titik lokasi gangguan jaringan listrik secara visual pada peta. penelitian ini dapat menghasilkan sebuah aplikasi web yang terintegrasi google map untuk dapat memberikan informasi secara real time yang terhubung pada konsumen.
PEMANFAATAN RASPBERRY PI PADA MODEL SISTEM MONITORING STABILITAS KEMIRINGAN KAPAL PENUMPANG UNTUK ANTISIPASI KECELAKAAN Abdurrasyid Abdurrasyid; Herman Bedi Agtriadi; Linda Alifiana
Prosiding Semnastek PROSIDING SEMNASTEK 2017
Publisher : Universitas Muhammadiyah Jakarta

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

Abstract

Salah satu penyebab kecelakaan di kapal adalah masalah ketidakstabilan kapal. Kapal tidak bisa dikendalikan karena ketidakseimbangan yang terjadi. Penempatan beban yang tidak wajar saat muatan dimuat ke kapal saat kapal bersandar di pelabuhan sering terjadi. Kecelakaan banyak terjadi diakibatkan karena kelalaian, petugas tidak mengetahui bagaimana posisi kestabilan kapal, sehingga penyelesaian masalah yang terjadi menjadi terlambat, masalah ini disebabkan oleh minimnya alat yang memberi peringatan kepada nahkoda dan awak kapal tentang masalah yang terjadi. Tujuan dari penelitian ini untuk memudahkan petugas kapal atau nahkoda untuk mengetahui serta memonitoring kondisi kapal dalam keadaan seimbang atau tidak seimbang. Penelitian ini menggunakan metode prototipe sebagai metode pengembangan perangkat lunak dan model rangkaian perangkat keras. PI Raspbery berfungsi sebagai server yang dikombinasikan dengan arduino sebagai pusat rangkaian pada kapal, dengan sensor gyroscope sebagai detektor kemiringan, selanjutnya data dikirim melalui modul wifi ke PI Raspbery sehingga data dapat diterima di smartphone android, pada waktu yang bersamaan buzzer memberi tanda peringatan saat kemiringan terjadi, pada saat melebihi toleransi kemiringan maka pemberat akan secara otomatis menyeimbangkan kapal sehingga kembali pada kondisi normal. Hasil yang diharapkan dari penelitian ini adalah model alat stabilitas pada kapal penumpang, dengan menggunakan raspberry sebagai server yang mengelola data kemiringan yang akan dikirimkan datanya ke smartphone android sehingga diharapkan dapat membantu nahkoda untuk memantau dan mengetahui keadaan kapal. Harapan dari penelitian ini adalah bisa dijadikan sebagai acuan untuk mengantisipasi kecelakaan pada kapal penumpang.Kata kunci: Monitoring, Stabilitas , Antisipasi kecelakan, Raspberry, Prototype
PELATIHAN MS. Office Word dan Excel BAGI PERANGKAT DESA & MASYARAKAT DESA CIARUTEUN ILIR BOGOR Max Teja Ajie; Efy Yosrita; Darma Rusjdi; Meilia Nur Indah Susanti; Indrianto Indrianto; Rizqia Cahyaningtyas; Dewi Arianti Wulandari; Herman Bedi Agtriadi
Terang Vol 1 No 1 (2018): TERANG : Jurnal Pengabdian Pada Masyarakat Menerangi Negeri
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (448.852 KB) | DOI: 10.33322/terang.v1i1.209

Abstract

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Gray level co-occurrence matrix feature extraction and histogram in breast cancer classification with ultrasonographic imagery Karina Djunaidi; Herman Bedi Agtriadi; Dwina Kuswardani; Yudhi S. Purwanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp795-800

Abstract

One way to detect breast cancer is using the Ultrasonography (USG) procedure, but the ultrasound image is susceptible to the noise speckles so that the interpretation and diagnosis results are different. This paper discusses the classification of breast cancer ultrasound images that aims to improve the accuracy of the identification of the type and level of cancer malignancies based on the features of its texture. The feature extraction process uses a histogram which then the results are calculated using the Gray Level Co-Occurrence Matrix (GLCM). The results of the two extraction features are then classified using K-Nearest Neighbors (KNN) to obtain accurate figures from those images. The results of this study is that the accuracy in detecting cancer types is 80%.
Advanced Credit Scoring with Naive Bayes Algorithm: Improving Accuracy and Reliability in Financial Risk Assessment Afandi, Adam; Bedi Agtriadi, Herman; Luqman, Luqman; Susanti, Meilia
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 8 No 2 (2024)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v8i2.2160

Abstract

This study develops a credit application recommendation system based on the Naive Bayes method to improve accuracy and reliability in financial risk assessment. Using the CRISP-DM framework, the research process starts with understanding the business needs to implement a web-based system. The Naive Bayes algorithm was chosen because of its ability to handle binary data classification and generate reliable predictions even with limited training data. This study combines feature selection and unbalanced data handling techniques to improve model performance. The evaluation results showed that the system achieved an accuracy of 70.50%, with a precision of 92.16%, a recall of 64.57%, and an F1-score of 75.83%. This system is implemented as a web-based application to help financial institutions make credit decisions quickly and accurately. These findings significantly contribute to developing a data-based classification system for the banking sector, especially in reducing the risk of bad loans and improving decision-making efficiency
Implementation of WYSIWYG in the Development of ITCC ITPLN Letter Management Information System Herman Bedi Agtriadi; Jatnika, Hendra; Yessy Fitriani; Zakiya Viantika Sihabudin4; Muhammad Nur Khanib; David Gabriel Sembiring; Ocha Nia Martcya Situmorang
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 5 No 2 (2021)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v5i2.2326

Abstract

Information Technology Certification Center (ITCC) is a laboratory focused on Certification activities in Information and Technology. Currently, ITCC uses a manual system with Microsoft Excel for creating letter numbers, which is time-consuming and prone to data loss due to unintegrated storage. To solve this, the author developed a letter archive application using the Rapid Application Development (RAD) model, which accelerates the development process by producing incremental software versions. This application improves the efficiency of numbering and securely storing mail archives while providing a WYSIWYG editor for easy document editing
Comparative Analysis of the Accuracy of Multiple Linear Regression Method and Ridge Regression Method in Predicting Dengue Fever Cases in South Tangerang City Dina Aulia; Herman Bedi Agtriadi; Luqman
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 1 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2292

Abstract

One of the main health issues in South Tangerang City is dengue fever (DBD). This study aims to compare the accuracy of Multiple Linear Regression and Ridge Regression methods in predicting the number of DBD cases using weather data such as temperature, humidity, and average rainfall. The data used is monthly data from South Tangerang City. The analysis process includes preprocessing, splitting the dataset into training and testing data, and applying both regression methods. To determine the prediction error rate, model accuracy is evaluated using the Mean Absolute Percentage Error (MAPE) metric. The results indicate that Ridge Regression performs better for datasets with high multicollinearity, yielding a MAPE value of 20.12%, while Multiple Linear Regression is more effective for datasets with low feature correlation, showing a MAPE value of 44.6%. This study provides important insights into selecting predictive techniques based on the characteristics of the analyzed dataset. It is hoped that this research can improve mitigation and planning for DHF cases in South Tangerang City by choosing the appropriate approach.
Literature Study: Prediction of the Type of Company where Students Work Using Naïve Bayes and Neural Network Algorithms Saputra, Angga; Luqman; Herman Bedi Agtriadi
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 1 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2314

Abstract

Research was conducted to evaluate the effectiveness of various machine learning algorithms, such as Naive Bayes, Support Vector Machine, Random Forest, and Artificial Neural Network (ANN), in predicting and classifying data. Naive Bayes proved to be efficient and accurate in structured data classification, such as predicting alumni's waiting time to get a job (94%) and vocational school students' job readiness (96.95%). On the other hand, neural network methods such as ANN and GRNN are superior in handling non-linear regression problems, such as house price prediction or college students' study period, although there is still room to improve accuracy. Random Forest is more suitable for complex data, while Naive Bayes is more effective for simple data. This research emphasizes the importance of selecting relevant variables, such as gender, major, and GPA, to improve model performance. Therefore, the selection of machine learning methods should be tailored to the type of data and the purpose of the analysis, as each algorithm has its own advantages and disadvantages.
A Herman Bedi Agtriadi; M Habibi; Zakiyah Misfazilah
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 1 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2501

Abstract

Breast cancer is the most common cancer globally with a malignant category that poses a serious and frightening threat to women. According to data from Globocan. In Indonesia alone in 2022 the number of new cases of breast cancer reached 66,271 cases, thus contributing (30,1.6%) of the total cancer cases in Indonesia. Of the cases with more than 22 thousand deaths, breast cancer is the second most deadly cancer. 70% of breast cancer cases are detected already at an advanced stage, where this case can occur due to delays in medical personnel who have not been able to detect breast cancer manually. This requires technology to help doctors and radiologists to evaluate Magnetic Resonance Imaging (MRI) images automatically. One of the deep learning methods useful for MRI image analysis is Convolutional Neural Network (CNN) using VGG19 and AlexNet architecture which has been proven in the classification process. This study uses data from Kaggle with a total of 1400 data. Through the use of the Convolutional Neural Network method, this study obtained a fairly optimal accuracy on the VGG19 architecture of 99% and on the AlexNet Architecture of 97%.
Implementation of Hybrid Recommendations in the Standardized Student Internship Assessment System At ITPLN Octaviasari, Afifah Nurlita; Agtriadi, Herman Bedi; Luqman; Jatnika, Hendra
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 9 No 1 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i1.2287

Abstract

Student internship assessment is an important aspect of higher education, requiring objective and accurate standards. This research examines implementing a hybrid recommendation system to improve the internship assessment process at ITPLN. The hybrid recommendation method combines content-based and collaborative approaches so that it can provide more relevant and personalized recommendations. Through analysis of previous assessment data and feedback from students and supervisors, this system is designed to assess student performance more comprehensively. The research results show that the use of a hybrid recommendation system can increase accuracy and fairness in assessments, as well as provide additional insight for supervisors in providing evaluations. Thus, this research contributes to the development of better assessment systems in the context of professional education, especially in the fields of engineering and technology. It is hoped that the implementation of this system can become a model for other institutions in optimizing the student internship assessment process.
Co-Authors Abdurrasyid Abdurrasyid, Abdurrasyid Afandi, Adam Afif Dwi Kurniawan Angga Saputra Chaerudin, Muhammad Farhan Darma Rusjdi David Gabriel Sembiring Dewi Arianti Wulandari Dimas Arditya Pinandhito Dina Aulia Djunaidi, Karina Dwina Kuswardani Efy Yosrita Febryansyah Hans Arieyantho, Febry Haris, Abdul Hendra Jatnika Indrianto Indrianto Indrianto Kusnandar, Adam Ramdani Linda Alifiana luqman Luqman, Luqman M Habibi Max Teja Ajie Meilia Nur Indah Susanti Muhammad Nur Khanib Ocha Nia Martcya Situmorang Octaviasari, Afifah Nurlita Purwanto, Yudhi Rahmad Evan Rizqia Cahyaningtyas Susanti, Meilia Yessy Fitriani Zakiya Viantika Sihabudin4 Zakiyah Misfazilah