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All Journal Jurnal Penelitian Fisika dan Aplikasinya (JPFA) TELKOMNIKA (Telecommunication Computing Electronics and Control) Abdimas Lembaran Ilmu Kependidikan Scientific Journal of Informatics Jurnal Kajian Informasi & Perpustakaan JURNAL PROFESI KEGURUAN Dinamisia: Jurnal Pengabdian Kepada Masyarakat JMM (Jurnal Masyarakat Mandiri) Journal of Innovation in Educational and Cultural Research Journal of Soft Computing Exploration International Journal of Engineering, Science and Information Technology Journal of Advances in Information Systems and Technology Proceeding of International Conference on Science, Education, and Technology Recursive Journal of Informatics Scientific Journal of Informatics Jurnal Abdi Negeri Recursive Journal of Informatics
Aji Purwinarko, Aji
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Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Implementation of Convolutional Neural Network Algorithm Using Vgg-16 Architecture for Image Classification in Facial Images Hapsari, Renita Arianti; Purwinarko, Aji
Recursive Journal of Informatics Vol 1 No 2 (2023): September 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v1i2.68059

Abstract

Abstract: Face Recognition has now become a technology capable of recognizing facial patterns, facial image recognition is also used in various applications, for example in biological data recognition applications, digital image and video search, room security, and other applications. Purpose: This study aims to find out how the implementation of the CNN method with the VGG-16 architecture affects the classification of gender in facial images and how it affects the results. Methods/Study design/approach: In this study, we use the CNN method for data processing and build the program and use VGG-16 Architecture to build the model, then the tensorflow library for calling the required features such as when optimizing or for statistical plots and using the Confusion Matrix to determine the level of accuracy obtained. The desired results in this study are accuracy, precision, recall, and Fscore. Result/Findings: Classifying facial images using CNN with VGG-16 architecture provides an accuracy rate of 94%. From the results of this study it can be concluded that the model with the best accuracy is at epoch 20 compared to epoch 60, epoch 80, and epoch 100 which have previously been tested. Novelty/Originality/Value: The level of accuracy resulting from the implementation of the CNN method using the VGG-16 Architecture for image classification in facial images is quite good, resulting in an accuracy of 94%. Accuracy results were obtained from tests carried out by comparing several epoch values to produce the best accuracy of 94% using epoch 20.
Crude oil price prediction using Artificial Neural Network-Backpropagation (ANN-BP) and Particle Swarm Optimization (PSO) methods Purwinarko, Aji; Amalia Langgundi, Fitri
Journal of Soft Computing Exploration Vol. 4 No. 2 (2023): June 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i2.159

Abstract

Crude oil price fluctuations significantly affect commodity market price fluctuations, so a sudden drop in oil prices will cause a slowdown in the economy and other commodities. This is very important for Indonesia, one of the world's oil-producing countries, to gain multiple benefits from oil exports when world oil prices increase and increase economic growth. Therefore, a system is needed to predict world crude oil prices. In this case, the Particle Swarm Optimization (PSO) algorithm is applied as the optimization of the weight parameters in the Artificial Neural Network-Backpropagation (ANN-BP) method. We compared the ANN-BP–PSO and ANN-BP methods to obtain the method with the best causation value based on the MAPE and MSE results. PSO aims to find the best weight value by iterating the process of finding and increasing position, speed, Pbest, and Gbest until the iteration is complete. The results showed that the ANN-BP-PSO process was classified as very good and had a lower predictive error rate than the ANN-BP method based on the MAPE and MSE values, which is 5.02007% and 7.15827% compared to 6.28323% and 13.86345.
Thermal Durability Characterization of a Simple Polymethyl-methacrylate (PMMA) Based-Optical Waveguide Yulianti, Ian; Insan , Shiva Maulana Khoiru; Putra, Ngurah Made Darma; Purwinarko, Aji; Widiarti, Nuni; Ngajikin, Nor Hafizah
Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Vol. 14 No. 2 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jpfa.v14n2.p113-124

Abstract

Polymethyl-methacrylate (PMMA)-based optical waveguide is a good candidate for a simple and low-cost waveguide. However, the thermal properties have not been investigated. In this work, thermal durability characterization of PMMA-based waveguide was carried out. Waveguide fabrication process was done in three stages, which are patterning the PMMA cladding, core material synthetization and core material application to the cladding. Core pattern with cross section area of 1×1 mm2 was engraved on the 4 cm long PMMA sheet. Unsaturated polyester resin (UPR) was used as a core material. Characterizations were conducted for temperature dependent loss (TDL), temperature working range, and long exposure durability. For TDL characterization, the temperature varied from 30°C to 75°C. Meanwhile, for temperature working range, the waveguide was exposed to cycled heating. The thermal durability characterization was done by immersing the waveguide in distilled water at temperature of 40 °C for 288 hours. The results showed that a little change of output intensity occurred due to temperature variation with TDL of 0.0235 dB/°C. The maximum limit of the temperature is 70°C. For long exposure to temperature of 40oC, the results showed that the waveguide has a good performance.
The Radio Frequency Identification Implementation Design for INLISLite Library Management System Majidah, Majidah; Widiyanto, Widiyanto; Purwinarko, Aji; Harto, Kasinyo; Fridiyanto, Fridiyanto; Mukminin, Amirul
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.902

Abstract

A challenge with self-borrowing services is the investment cost for both software and hardware. A software license is required to connect INLIS [Integrated Library System] Lite with Radio Frequency Identification [RFID], which results in a high cost, especially when the software is only compatible with specific RFID devices. This study aims to look at the INLISLite Library Information System, focusing on implementing a self-borrowing service using microcontrollers and RFID technology. In this research, system development uses the prototyping method. This study developed a self-borrowing module for INLISLite without the need for licensed connector software. Additionally, the module is compatible with various microcontrollers and RFID devices that are readily available. The research proposes a novel model that utilizes RFID technology and NodeMCU ESP8266 for the INLISLite Library Information System. RFID sensors read book data from tags, while the NodeMCU microcontroller facilitates communication between the RFID system and the server, allowing automatic transmission of book data to the INLISLite database. This setup enables seamless self-service borrowing, which was tested successfully, supporting processes such as logging in, scanning book data, and updating loan status in the database.
Integrating C4.5 and K-Nearest Neighbor Imputation with Relief Feature Selection for Enhancing Breast Cancer Diagnosis Purwinarko, Aji; Budiman, Kholiq; Widiyatmoko, Arif; Sasi, Fitri Arum; Hardyanto, Wahyu
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.21673

Abstract

Purpose: Breast cancer remains a significant cause of mortality among women, requiring accurate diagnostic methods. Traditional classification models often face accuracy challenges due to missing values and irrelevant features. This investigation advances the classification of breast cancer through the amalgamation of the C4.5 algorithm with K-Nearest Neighbor (KNN) imputation and Relief feature selection methodologies, thereby augmenting data integrity and enhancing classification efficacy. Methods: The Wisconsin Breast Cancer Database (WBCD) was the core reference for evaluating the proposed methodology. KNN imputation addressed missing values, while Relief selected the most relevant features. The C4.5 algorithm executed training by utilizing data segregations in the corresponding proportions of 70:30, 80:20, and 90:10, with its efficiency gauged through a range of metrics, particularly accuracy, precision, recall, and F1-score. Result: This innovative methodology achieved the highest classification accuracy of 98.57%, surpassing several existing models. Particularly noteworthy, the strategy being analyzed exhibited remarkable success relative to PSO-C4.5 (96.49%), EBL-RBFNN (98.40%), Gaussian Naïve Bayes (97.50%), and t-SNE (98.20%), demonstrating associated advancements of 2.08%, 0.17%, 1.07%, and 0.37%. These results confirm its effectiveness in handling missing values and selecting relevant features. Novelty: Unlike prior studies that addressed missing values and feature selection separately, this research integrates both techniques, enhancing classification accuracy and computational efficiency. The findings suggest that this approach provides a reliable breast cancer diagnosis method. Future work could explore deep learning integration and validation on larger datasets to improve generalizability.
Measuring The Acceptance Level of User Interface Design of ERP System at PT Allure Alluminio Using Technology Acceptance Model (TAM) Method Mutoriq, Alfat; Purwinarko, Aji
Journal of Advances in Information Systems and Technology Vol 5 No 2 (2023): October
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v5i2.71096

Abstract

The utilization of information technology and the internet in the industry is generally done through the implementation of Enterprise Resource Planning (ERP) systems to manage business processes. The success of ERP system implementation requires efforts to support and prevent failure risks. One of the risk factors for failure is the User Interface (UI) design of the ERP system. Good UI design that meets user needs needs to be considered. This research utilizes the Design Thinking (DT) method to create a good UI design and the Technology Acceptance Model (TAM) method to measure the acceptance of UI design in the manufacturing module of PT Allure Alluminio. The DT method was chosen as the UI design method for the ERP system because it is considered more creative in generating product concepts compared to other standard methods and TAM was chosen as one of the best methods for explaining technology acceptance and is a popular and commonly used approach. The UI design process is carried out in 6 phases of DT under the guidance of the Person in Charge (PIC), and the acceptance of UI design is measured after system implementation. Acceptance measurement is conducted using TAM with navigation and UI design variables as external variables, which are taken from previous research, and it tests the variables of Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Behavioral Intention to Use (BITU) among 50 users of the ERP system in the manufacturing module at PT Allure Alluminio through a Google Form questionnaire. The collected data is analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 3.2.9. The research results indicate that the DT method is effective for UI design as it involves users. The analysis shows that the use of ERP systems is influenced by perceived usefulness and ease of use, but not influenced by UI design. Navigation and UI design provide ease of use.
- Product Diversification and Marketing Methods of Jamu Gendong in Kampung Jamu Semarang: DIVERSIFIKASI PRODUK OLAHAN JAMU GENDONG DAN METODE PEMASARANNYA DI KAMPUNG JAMU SEMARANG Marianti, Aditya; Susanti, R; Purwinarko, Aji; Dimarti, Safira Chairani; Sasi, Fitri Arum; Yuliantika, Atika; Hasani, Azharu Alfi; Rahmawati, Sinta Kurnia
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 8 No. 1 (2024): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v8i1.11605

Abstract

Kampung jamu di kelurahan Wonolopo kecamatan Mijen Semarang adalah sentra produsen jamu Gendong. Wilayah pemasarannya masih di Semarang karena produk tidak awet, sehingga berpotensi menurunkan daya saing dengan produk pabrikan. Tujuan kegiatan pengabdian kepada masyarakat (PPM) ini adalah melatih keterampilan mitra membuat jamu serbuk yang berkualitas, mengemasnya dan dapat memasarkan produknya secara online sehingga wilayah pemasarannya lebih luas Metode kegiatan PPM adalah pelatihan membuat jamu serbuk dan pengemasannya serta pelatihan pembuatan branding dan berjualan online. Pelatihan dilaksanakan pada 15 Juli dan 3 September 2022, kepada 15 orang anggota Paguyuban produsen jamu gendong “Sumber Husodo”. Hasil pelatihan adalah peserta dapat mempraktekkan membuat jamu serbuk dan mengemasnya. Peserta pelatihan juga dapat membuat branding produk jamunya. Peserta juga dapat mempraktekkan penggunaan aplikasi berjualan online, menggunakan facebook, instagram dan tiktok. Simpulan dari kegiatan PPM ini adalah mitra memiliki bekal untuk melakukan diversifikasi produk dan memperluas wilayah pemasarannya, sehingga diharapkan dapat bertahan dan bersaing dengan produk jamu pabrikan. Kata kunci: Kampung Jamu, Semarang, diversifikasi produk, pemasaran online
Peningkatan Manajemen Ujian Online Bagi Guru di SMK Negeri 1 Karimunjawa Prasetiyo, Budi; Hakim, M. Faris Al; Purwinarko, Aji; Putra, Anggyi Trisnawan; Subhan, Subhan
Jurnal Abdi Negeri Vol 1 No 1 (2023): Januari 2023
Publisher : Informa Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63350/jan.v1i1.3

Abstract

Program Pembelajaran dan Penilaian online di era pandemi COVID-19 saat ini telah berlangsung selama satu tahun di SMK Negeri 1 Karimunjawa. Penerapan e-learning di sekolah ini berdampak pada bertambahnya tuntutan peningkatan kompetensi guru dalam melakukan penilaian secara online. Bagi siswa, tentu akan berdampak terhadap cara mereka mengikuti ujian di sekolah. Siswa harus membiasakan diri untuk menggunakan fitur-fitur yang terdapat pada aplikasi e-learning. Berdasarkan hasil komunikasi dan observasi dengan Kepala SMK Negeri 1 Karimunjawa, dibutuhkan aplikasi yang efektif untuk digunakan dalam penyelenggaraan penilaian atau ujian online untuk siswa. Aplikasi Ujian online juga diharapkan dapat digunakan untuk simulasi Asesmen Kompetensi Minimal (AKM) pada tahun pelajaran 2021/2022. Oleh karena itu, Jurusan Ilmu Komputer, FMIPA, UNNES menawarkan solusi berupa penerapan aplikasi e-ujian yang merupakan produk penelitian yang telah memiliki hak cipta. Metode yang digunakan dalam kegiatan pengabdian ini terdiri dari 3 tahap yaitu Analisis Kebutuhan, Perancangan Aplikasi, Pengembangan Aplikasi, Pelaksanaan, dan Evaluasi. Hasil dari kegiatan pengabdian masyarakat yang telah dilaksanakan adalah pengurus sekolah dan guru memahami potensi dari manajemen ujian berbasis daring untuk pembelajaran di masa pandemi sebagai upaya untuk menjaga standar proses pembelajaran.
Selection of Trading Indicators Using Machine Learning and Stock Close Price Prediction with the Long Short Term Memory Method Alfandy Himawan Bagus Rafli; Aji Purwinarko
Recursive Journal of Informatics Vol. 3 No. 2 (2025): September 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i2.945

Abstract

Abstract. Humans have a limit to their physical ability to work, so investment is needed to meet their needs and other goals according to their wants and needs. Investment has many types and risks according to the portion of the return value, such as mutual funds, bonds and stocks. Stocks are a form of investment that has a high risk because of the rapid fluctuations in stock values. Prediction of stock movements is usually assisted by indicators, but predictions using indicators require complex analysis because of the diverse periods and different movements in each stock data case.  Purpose: To predict the closing price of BBCA and BBRI shares in the next 10 days by considering the count of technical indicators in the form of Moving average (MA), Exponential moving average (EMA), Rate Of Change (ROC), Price Momentum, Relative Strength Index (RSI), Stochastic Oscillator in periods 21, 63 and 252. Methods/Study design/approach: This research was conducted by comparing the accuracy of Random Forest, Decision Tree, KNN, SVM using K-fold Cross Validation then the method with the best accuracy was used to find out how much velue from the trading indicators used and predict the closing price of shares per day at BBRI and BBCA companies for the next 10 day period using the LSTM algorithm. Result/Findings: The best accuracy in the k-fold cross validation process is random forest. random forest is used to train indicator data in determining 5 indicators along with the period that has the highest value, in this test it produces values on BBCA data in order, namely ROC63, RSI63, MOM63, MA252, EMA21 while on BBRI data in order, namely ROC63, MOM63, RSI63, MA252, MA21. This indicator is used in the price forecasting process with the LSTM method to determine the closing price in the next 10 days. The LSTM method in this study resulted in 96.8% accuracy for BBCA and 96.4% accuracy for BBRI. Novelty/Originality/Value: The forecasting accuracy on BBCA is 96.8% and the forecasting accuracy on BBRI is 96.4%. This shows that the accuracy results are classified as good because the prediction results are close to the actual results. The data training process is expected to help traders in making stock buying and selling decisions that are adjusted to the fundamental aspects of the company.
Random Forest Algorithm Optimization using K-Nearest Neighborand SMOTE on Diabetes Disease Syuja Zhafran Rakha Krishandhie; Aji Purwinarko
Recursive Journal of Informatics Vol. 3 No. 1 (2025): March 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i1.1576

Abstract

Abstract. Diabetes is a chronic disease that can cause long-term damage, dysfunction and failure of various organs in the body. Diabetes occurs due to an increase in blood sugar (glucose) levels exceeding normal values. Early diagnosis of diseases is crucial for addressing them, especially in the case of diabetes, which is one of the chronic illnesses. Purpose: This study aims to find out how the implementation of the K-Nearest Neighbor algorithm with the Synthetic Minority Oversampling Technique (SMOTE) in optimizing Random Forest algorithm for diabetes disease prediction. Methods/Study design/approach: This study uses the Pima Indian Diabetes Dataset, the random forest algorithm for the classification, k-nearest neighbor for optimization, and SMOTE for the minority class oversampling. Result/Findings: The prediction accuracy of the model using SMOTE and k-nearest neighbor is 92,86%. Meanwhile, the model that does not use SMOTE and k-nearest neighbor obtains an accuracy of 83,03%. Novelty/Originality/Value: This research shows that the use of random forest algorithm with k-nearest neighbor and SMOTE gives better accuracy than without using k-nearest neighbor and SMOTE.
Co-Authors Adi, Mahargjo Hapsoro Aditya Marianti Akhmad Munawar Alfa Narendra Alfandy Himawan Bagus Rafli Amalia Langgundi, Fitri Amidi Amidi Amin Retnoningsih Amirul Mukminin Anggyi Trisnawan Putra Annindya Ardiansari Ardhi Prabowo Arif Widiyatmoko, Arif Arsyadani, Fathan Aryono Adhi, Aryono Bambang Sugiantoro Budi Prasetiyo, Budi Budiman, Kholiq Cahyani, Rima Ayu Damayanti, Dela Rista Dela Rista Damayanti Dimarti, Safira Chairani Edi Subkhan, Edi Fathan Arsyadani Fianti Fianti, Fianti Fitri Arum Sasi, Fitri Arum Fridiyanto, Fridiyanto Hakim, M. Faris Al Hapsari, Renita Arianti Hasan Mukhibad Hasani, Azharu Alfi Ian Yulianti, Ian Indah Urwatin Wusqo Insan , Shiva Maulana Khoiru Kasinyo Harto Langlang Handayani Lutfia Nur Hadiyanti, Lutfia Nur Mahargjo Hapsoro Adi Majidah Majidah Martien Herna Susanti Melati, Inaya Sari Melissa Salma Darmawan MHD. Reza M.I. Pulungan Muhamad Taufiq Munawar, Akhmad Mutoriq, Alfat Nor Hafizah Ngajikin Nuni Widiarti Nur Kusuma Dewi Putra, Ngurah Made Darma Rahmawati, Sinta Kurnia Renita Arianti Hapsari Rima Ayu Cahyani Ririanti, Novia Puji Sri Siswati Subagja, Mona Subhan Subhan Sugiyarta Stanislaus, Sugiyarta Sujarwata Sujarwata Sukestiyarno Sukestiyarno Susilo Susilo Syuja Zhafran Rakha Krishandhie Syukron Salam, Syukron Tris Sugiarto Tsabit Azinar Ahmad Utis Sutisna Wahyu Hardyanto Widiyanto Widiyanto Woro Sumarni Yanuarto, Alfath YB. Praharto Yuliantika, Atika