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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Teknologi dan Manajemen Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Prosiding SNATIF Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan PROtek : Jurnal Ilmiah Teknik Elektro Sistem : Jurnal Ilmu-Ilmu Teknik JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science) Conference SENATIK STT Adisutjipto Yogyakarta Management and Economics Journal (MEC-J) JTAM (Jurnal Teori dan Aplikasi Matematika) JURNAL INSTEK (Informatika Sains dan Teknologi) CYCLOTRON Jurnal Abadimas Adi Buana Jiko (Jurnal Informatika dan komputer) Jurnal Teknik Elektro dan Komputer TRIAC JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) bit-Tech Jurnal Sistem Informasi dan Informatika (SIMIKA) JATI (Jurnal Mahasiswa Teknik Informatika) CIVITAS (JURNAL PEMBELAJARAN DAN ILMU CIVIC) International Journal of Advances in Data and Information Systems Journal of Computer Networks, Architecture and High Performance Computing Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat JAREE (Journal on Advanced Research in Electrical Engineering) Jurnal Teknik Informatika (JUTIF) International Journal of Robotics and Control Systems Jurnal Teknologi dan Manajemen ALINIER: Journal of Artificial Intelligence & Applications SinarFe7 Jurnal Penelitian Journal of Information Systems and Technology Research Jurnal Informatika Teknologi dan Sains (Jinteks) JAPI: Jurnal Akses Pengabdian Indonesia Jurnal Ilmiah Edutic : Pendidikan dan Informatika Internet of Things and Artificial Intelligence Journal JEECS (Journal of Electrical Engineering and Computer Sciences) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Information Technology International Journal (ITIJ) Al-Zayn: Jurnal Ilmu Sosial & Hukum Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal ilmiah teknologi informasi Asia Jurnal PETISI (Pendidikan Teknologi Informasi) Jurnal Aplikasi Sains Data
Dwi Arman Prasetya
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Stock Price Prediction and Risk Estimation Using Hybrid CNN-LSTM and VaR-ECF Febriyanti, Alvi Yuana; Prasetya, Dwi Arman; Trimono, Trimono
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4648

Abstract

Stock price prediction is a major challenge in the financial domain due to high volatility and complex movement patterns. Traditional methods such as fundamental and technical analysis often fail to capture the non-linear characteristics and fast-changing market dynamics, highlighting the need for more adaptive approaches. This study proposes a hybrid deep learning model, CNN-LSTM, which combines CNN's local feature extraction capabilities with LSTM’s ability to model long-term temporal dependencies. To incorporate risk management, the model is also integrated with the Value at Risk (VaR) approach using the Cornish-Fisher Expansion (ECF) to estimate potential losses under extreme market conditions. The study utilizes daily historical stock price data of PT Unilever Indonesia Tbk retrieved from Yahoo Finance. Model performance is evaluated using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), where the model achieves an MAE of 78.13 and a MAPE of 2.72%, indicating relatively low absolute and relative prediction errors. These results confirm that the CNN-LSTM approach effectively models stock price movements in dynamic market environments, and the integration with VaR-ECF provides a more comprehensive risk estimate. Thus, this approach not only enhances predictive accuracy but also offers valuable decision-support tools for investors in planning investment strategies.
Develop IoT-Based Automatic Water Gate Control Prototype with Fuzzy Logic Approach Ismail, Jefri Abdurrozak; Aditya, Wigananda Firdaus Putra; Ekawati, Anies; Sari, Anggraini Puspita; Prasetya, Dwi Arman
Jurnal Teknologi dan Manajemen Vol 6, No 1 (2025): January
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat ITATS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.jtm.2025.v6i1.6758

Abstract

This research developed a prototype for an automatic water gate control system that integrates Internet of Things (IoT) technology with a Fuzzy Logic approach. The prototype is designed to monitor and regulate water levels in real-time using ultrasonic sensors connected to an IoT network. The water level data is integrated into Amazon Web Services (AWS) for cloud management. Fuzzy Logic was chosen to enhance the system's accuracy and responsiveness to dynamic and unpredictable water levels. The primary goal of this system is to minimize flood risk and ensure adequate water distribution across various sectors by automatically opening the water gates. In initial testing, the prototype successfully transmitted water level data from the sensors to the AWS cloud server and performed fuzzy calculations according to Fuzzy Logic formulas. The prototype demonstrated good results in managing the opening of the water gates based on the water levels detected by the ultrasonic sensors, showing significant potential for water resource management in urban areas through this system.
IMAGE CLASSIFICATION OF VINE LEAF DISEASES USING COMPLEX-VALUED NEURAL NETWORK Putri, Irma Amanda; Prasetya, Dwi Arman; Fahrudin, Tresna Maulana
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7809

Abstract

Leaf diseases are a serious challenge in the agricultural industry affecting crop quality and yield especially in grapevines. Early recognition and classification of grape leaf diseases is crucial to enable farmers to take appropriate preventive measures in maintaining the health of their crops. The research utilized an innovative approach based on Complex-Valued Neural Network (CVNN) to address the problem. Using Complex-Valued Neural Network (CVNN) this research seeks to identify and classify grape leaf diseases through a series of experiments. A total of 100 images divided into 4 classes namely Black Rot, ESCA, Leaf Blight, and Healthy were collected to train the model. The results show that the trained CVNN model successfully achieved a training accuracy of 100% and a testing accuracy of 97%, demonstrating excellent performance in classifying grape leaf diseases. This states that the proposed approach has great potential to be an effective tool in helping growers manage their vineyards more efficiently and effectively. The developed image processing method is expected to be applied in designing a system to perform image classification of diseases on grape leaves.
CLASSIFICATION OF JAVANESE NGLEGENA SCRIPT USING COMPLEXVALUED NEURAL NETWORK Rahmawati, Adinda Aulia; Muhaimin, Amri; Prasetya, Dwi Arman
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7808

Abstract

Javanese script is one of the traditional scripts in Indonesia used by the Javanese people. The Javanese script used in Javanese spelling basically consists of 20 main characters (nglegena), namely from the Ha to Nga script. Javanese script has very high value, the uniqueness of the script is one thing that must be preserved. However, widespread use of Javanese script has declined as technology has developed. In this context, one of the problems that arises is the difficulty in automatically recognizing and classifying the Javanese Nglegena script. Therefore, the use of computational methods to automatically classify the Nglegena Javanese script is very important. This research compares 2 methods for classifying Javanese Nglegena script, namely Complex-Valued Neural Network (CVNN) and Convolutional Neural Network (CNN). This research aims to compare the best accuracy between CVNN and CNN. In this study, the Complex-Valued Neural Network method had a higher average accuracy, namely 96.332% and a loss of 0.1834. Meanwhile, the CNN method has an average accuracy of 93.72% and a loss of 0.4254. Artificial intelligence-based Javanese Nglegena script classification technology can help people to recognize the Javanese Nglegena script, especially in the fields of education and culture.
Optimizing Categorical Boosting Model with Optuna for Anti-Tuberculosis Drugs Classification Yosua Satria Bara Harmoni; Kartika Maulida Hindrayani; Dwi Arman Prasetya
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.ijeeemi.v7i2.92

Abstract

Tuberculosis is one of the leading causes of death globally, with death rate reaching 1.30 million by 2022, an increase of 3.2% compared to the previous year. Indonesia is one of the countries with the highest number of tuberculosis cases in the world. The Directly Observed Treatment Short-course (DOTS) plays a role in improving the effectiveness of tuberculosis therapy by ensuring the availability of appropriate anti-tuberculosis drugs. However, errors in drug selection can lead to therapy failure, relapse, and Multi-Drug Resistant (MDR) cases. To overcome this, classification models based on patient medical record data can be used to improve the accuracy of drug selection. This research focuses on developing classification model to determine the type of drug using Categorical Boosting algorithm optimized with Optuna using Tree-structured Parzen Estimator. The data consisted of numerical variables, such as age, treatment duration, and categorical variables, such as history of diabetes mellitus, HIV status, drug combination. The CatBoost algorithm was chosen due to its ability to handle categorical data. Hyperparameter optimization was performed to obtain the best parameters. The preprocessing stage involved memory reduction, feature normalization, and encoding on 620 data samples, which were then divided into 90% training and 10% test data. Experimental results show CatBoost model produces an initial accuracy of 90%. After applying parameter optimization techniques using Optuna, the accuracy increased to 96%, showing 6% improvement. The model is able to accurately classify drugs combination, which can support the selection of more effective therapies for tuberculosis patients. Thus, the use of SMOTE to address class imbalance combined with Optuna for hyperparameter optimization was shown to improve the accuracy of CatBoost-based classification models. This finding confirms the effectiveness of SMOTE and Optuna methods in improving the accuracy of prediction models for drug type classification, contributing the improvement of tuberculosis treatment strategies.
Implementation of Transfer Function ARIMA Model for Stock Price Prediction Azizah, Alisa Jihan; Prasetya, Dwi Arman; Hindrayani, Kartika Maulida; Fahrudin, Tresna Maulana
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1396

Abstract

Dynamic economic growth requires stable financing sources, one of which is through the capital market. In stock investment activities, risk and return are two fundamental aspects that are interrelated and must be carefully considered. The volatility of ASII stock prices, influenced by various factors including exchange rates, can create uncertainty in investment decision-making. This study aims to predict the stock price of PT Astra International Tbk (ASII) using a transfer function model approach that integrates the influence of the Indonesian rupiah to US dollar exchange rate as an external variable. The transfer function model is an extension of the ARIMA model that can measure the dynamic relationship between input and output variables. Based on the estimation results, the best model obtained has a transfer function order of (b,s,r) = (1,0,0) with a noise series of (p_n,q_n) = (1,0). The prediction results show that ASII stock price movements tend to be stable with a gradual decline over the next 20 days. Model evaluation demonstrates low error rates, with MAE of 84.19, RMSE of 110.37, and MAPE of 1.65%. These results indicate that the transfer function model is effective in modeling and predicting short-term stock prices with reasonably good accuracy.
OPTICS-Based Clustering of East Java Regencies and Cities by Divorce Factors Nurhalizah, Cesaria Deby; Damaliana, Aviolla Terza; Prasetya, Dwi Arman
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1227

Abstract

Divorce is a social phenomenon that occurs when a married couple decides to legally end their marriage. This decision is influenced by various factors such as conflict, economic pressure, domestic violence, and deviant behavior. The aim of this study is to group regencies and cities in East Java Province that share similarities in the main causes of divorce, in order to understand the patterns that emerge across regions. The OPTICS (Ordering Points to Identify the Clustering Structure) clustering method was chosen for its ability to identify cluster structures with varying densities. The modeling process was conducted using a proportion-based approach for each causal factor, with optimal parameters obtained through manual grid search using min_samples = 2, xi = 0.05, and min_cluster_size = 0.1. The analysis identified three main clusters, each dominated by conflict, economic hardship, and deviant behavior, respectively. The quality of the clustering was evaluated using a Silhouette Score of 0.588, indicating reasonably good results. These findings are expected to serve as an initial understanding of divorce causes in East Java and can be used as input for the formulation of more targeted social policies.
Prediksi Gangguan Kesehatan Mental pada Kalangan Mahasiswa Menggunakan Metode Pseudo-Labeling dan Algoritma Regresi Logistik Sari, Anggraini Puspita; Prasetya, Dwi Arman; Aditiawan, Firza Prima; Al Haromainy, Muhammad Muharrom
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp40-48

Abstract

Mental illness is a health condition that alters a person's thoughts, feelings, or behaviors, leading to distress and difficulty in maintaining a normal life. Mental health issues should not be taken lightly due to the challenges associated with diagnosis. Many students tend to experience mental health problems at various stages of their education, from diploma programs to doctoral studies. This situation becomes more critical as students approach the end of their studies and anticipate future prospects. This article explores the mental health status of students through symptoms, using logistic regression methods for prediction based on the dataset used. In this study, two types of data are employed: labeled dataset and unlabeled dataset, which are combined to create a semi-supervised learning approach. Labeled dataset is classified using a logistic regression algorithm, while unlabeled dataset employs the pseudo-labeling method. The analysis and modeling of the dataset indicate that the comparison between labeled and unlabeled dataset can significantly affect accuracy and processing time. Furthermore, the use of the pseudo-labeling method with the logistic regression algorithm is well-suited for the mental health case study, achieving an accuracy of 98% with a labeled to unlabeled dataset ratio of 1:2.
Comparison of Elbow and Silhouette Methods in Optimizing K-Prototype Clustering for Customer Transactions Kuswardana, Dendy Arizki; Prasetya, Dwi Arman; Trimono, Trimono; Diyasa, I Gede Susrama Mas
EDUTIC Vol 12, No 1: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i1.29744

Abstract

This research presents a comparative analysis of the Elbow and Silhouette methods to identify the ideal number of clusters in applying the K-Prototypes algorithm for customer grouping using purchase transaction data. The K-Prototypes algorithm is employed due to its ability to handle both numerical and categorical data simultaneously. Customer purchase transaction data from the Point of Sale (POS) system is analyzed through preprocessing, feature transformation, and attribute segmentation stages before being clustered using the K-Prototypes algorithm. To identify the optimal cluster count, this study employs two methods: the Elbow and the Silhouette method. The results indicate that the Elbow method produces 2 clusters with a model evaluation score of 0.6368, while the Silhouette method suggests 2 clusters with a slightly lower score of 0.6186. In terms of computational efficiency, the Elbow method also demonstrates a faster processing time results highlight the significance of choosing an appropriate method for identifying the ideal number of clusters, ensuring it aligns with the specific goals of the analysis, whether emphasizing superior inter-cluster distinction or favoring a more parsimonious model configuration.
Antenna 5.8 GHz dengan Output Perbedaan Fasa 90 Derajat Ganda Menggunakan Jaringan Matriks Butler Feeding Mujahidin, Irfan; Prasetya, Dwi Arman; Arifuddin, Rahman; Arinda, Putri Surya
PROtek : Jurnal Ilmiah Teknik Elektro Vol 8, No 2 (2021): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v8i2.3180

Abstract

A complex design of electromagnetic feeding network circuit components is needed for a wireless communication network system, and expensive materials, especially for electromagnetic components at a high frequency of 5.8 GHz with dual 90-degree phase difference output using butler matrix Feeding Network for wireless communication network systems. It is novel in form and has a frequency-free, frequency-free, non-complex configuration of microstrip electromagnetic circuits, and uses inexpensive materials at a frequency of 5.8 GHz. This system is a package consisting of a 5.8 GHz microstrip antenna with a rectangular microstrip antenna array and a Butler matrix of four inputs and outputs to achieve a double 90-degree phase difference. The antenna uses a via hole for the transmission line to the network feeding system, has one band and shallow bandwidth with four ports, and has one working frequency, namely 5.58 GHz under the S criterion -10dB, and uses a via hole for the transmission line to the network feeding system, the radiation pattern is forward, the gain level is 6.83dB, and the feeding impedance is 50 Ohm. S11 -26.19 dB for the 90-degree hybrid coupler, S21 31.65 dB, S51 -26.18 dB, and S61 32.52 dB which means these are all working well, and the overall size of this structure is 80mm x 65mm with FR4 of 4.4 dielectric constant having 50 ohms.
Co-Authors ', Nachrowie ., Humaidi A. A. Ngurah Gunawan Aan Nehru Awanto Achmad Junaidi Adelia Yuandhika Adhigiadany, Chelsea Ayu Aditya, Wigananda Firdaus Putra Afidria, Zulfa Febi Agustin, Sesillia Akio Kitagawa Alam, Fajar Indra Nur Alfa, Aniysah Fauziyyah Alhamda, Denisa Septalian Ali, Munawar Amrullah, Ahmad Wildan Andre Leto Andrew Arjunanda Yasin Anggraini Puspita Sari Anindha Lazuardi Aries Boedi Setiawan Arifani, Kahpi Baiquni Arifuddin, Rahman Arinda, Putri Surya Arum Puspita Ayu Aryananda, Rangga Laksana Asfiani, Ilil Musyarof Atiana Sofia Kaci Aviolla Terza Damaliana Awang, Wan Suryani Wan Azizah, Alisa Jihan Baidowi Baidowi Baidowi Baidowi Bambang Nurdewanto Barus, Indra Basitha F Hidayatulail Cahya Eka Melati Cahyani Kuswardhani, Hajjar Ayu cahyono, wahyu eko Candra Laksana Dafa Zain Musyafa Damai Arbaus, Damai Damaliana, Aviolla Terza Danang - Destiawan Danang Destiawan Datia Putri Nabila Br Tarigan Desi Tristianti Desyderius Minggu Dicky Kurniawan Diyasa, I Gede Susrama Mas Dody Pintarko Dwi Agung Ayubi E, Nachrowie Eka Prakarsa Mandyartha Ekawati, Anies Eko Wahyu Prasetyo Elta Sonalitha Sonalitha Emilia, Kholidatus Erik Roma Hurmuzi Erika Fatimatul Hidayanti Fadlila Agustina Fahrudin, Tresna Maulana Farhans, Muhammad Izzudin Febriyanti, Alvi Yuana Firdaus Firdaus Firza Prima Aditiawan Fitrah, Hazza Gatut Yulisusianto Halim, Christina Hari Fitria Windi Hendry Yudha Pratama Herdianti, Rahmalia Anindya Hesti Sholikah, Hesti Hidayatulail, Basitha F Hikmata Tartila Hiroshi Suzuki Hurmuzi, Erik Roma I Gede Susrama Mas Diyasa Ibrahim, Mohd Zamri Bin idhom, Mohammad Indra Barus Irsyadi, Muhamad Haidir Ismail, Jefri Abdurrozak Januar, Teddy Jariyah Jeki Saputra Junita Junita Kartika Maulida Hindrayani Kartika Maulida Hindrayani Kassim, Anuar bin Mohamed Kholid, Fajar Kukuh Yudhistiro, Kukuh Kurniawan, Dicky Kusuma, Dwi Febri Chandra Kusuma, Firdaus Miftakh Kuswardana, Dendy Arizki Laksana, Candra Larasati Lestari, Amanda Ayu Dewi Lisanthoni, Angela Luqna Aziziyah Maulidiyyah, Nova Auliyatul Millani, Alief Indy Mohammad Ansori Mohammad Idhom Mohammad, Bawazir Fadhil Muhaimin, Amri Muhammad Ansori Muhammad Ghinan Navsih Muhammad Muharrom Al Haromainy Muhammad Naswan Izzudin Akmal Mulyadi Mulyadi Nachrowie Nachrowie Nachrowie, Nachrowie Nambo Hidetaka Narumi Hayakawa Nauval Theo Jovaldi Nezalfa Sabrina Niken Sulistyowati Ningrum, Imelda Widya Ninik Sisharini Ninis Herawati Norma Windiyanti Novita Anggraini Nur Rachman Nur Rachman Supatmana Muda Nur Rochman Nur Rochman Nurhalizah, Cesaria Deby Permana, Iwan Setiawan Prakoso, Akbar Tri Prameswari, Diajeng Prismahardi Aji Riyantoko Puput Dani Prasetyo Adi Puput Marina Azlia Sari Putri Lestari Putri, Irma Amanda Putri, Serlinda Mareta Rabi, Abd. Rafli, Muhammad Rahayu Sri Utami Rahayu, Ayu Sri Rahman Arifuddin Rahmanda Putri, Endin Rahmawati, Adinda Aulia Respati Respati Ristiyani, Sintiya Riyantoko, Prismahardi Aji Rosariawari, Firra Rudi Wilson Sagita Rochman Salim, Hotimah Masdan Santika, Surya Sari, Andina Paramita Sigit, Syauqita Siswanto Siswanto Sitanggang, Desi Daomara Siti Nuurlaily Rukmana, Siti Nuurlaily Stanislaus Yoseph Subairi Subairi Sugiarto S Sumartono Sumartono Sumartono Suprayogi Suprayogi Suprayogi Suprayogi Surya Nanda Santika, Surya Suryantari, Putu Anggi Takahiro Kitajima Takashi Yasuno Tresna Maulana Fahrudin Trimono Trimono Trimono, Trimono Utomo, Setyobudi Wahyu Dirgantara Wahyu Putra Pratama Wahyu Syaifullah Jauharis Saputra Wahyuni, Dinar H S wangge, ferdinandus Weisrawei, Yosef Yasin, Andrew Arjunanda Yohanes U D Sipul Yosef Weisrawei Yosua Satria Bara Harmoni Yuliani, Devina Putri Yunia Dwie Nurchayanie Yusaq Tomo Ardianto