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Jurnal Teknologi dan Manajemen Informatika
Published by Universitas Merdeka Malang
ISSN : 16936604     EISSN : 25808044     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Jurnal Teknologi dan Manajemen Informatika (JTMI) diterbitkan oleh Fakultas Teknologi Informasi Universitas Merdeka Malang. JTMI terbit 2 edisi per tahun pada Januari - Juni dan Juli - Desember dengan scope ilmu komputer yang mencakup teknologi informasi, sistem informasi, dan manajemen informatika.
Arjuna Subject : -
Articles 141 Documents
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Klasifikasi Cacat Biji Kopi Menggunakan Metode Transfer Learning dengan Hyperparameter Tuning Gridsearch Michael, Aryo; Rusman, Juprianus
Jurnal Teknologi dan Manajemen Informatika Vol. 9 No. 1 (2023): Juni 2023
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v9i1.10035

Abstract

Defects in coffee beans can significantly impact the quality of coffee production, which can lead to a decrease in the price of coffee beans in the global coffee market. Currently, coffee bean sorting is still conventionally done to separate defective and non-defective coffee beans, which is a time-consuming process and subject to subjective selection, potentially leading to a decline in the quality of the resulting coffee beans. The objective of this research is to design and measure the performance of deep learning algorithms, CNN MobilNetV2 and DenseNet201, using transfer learning methods where hyperparameter tuning grid search is employed to select the optimal combination of hyperparameters for the defective coffee bean classification model. The study began by collecting a dataset of images of abnormal and defective coffee beans, building a classification model using transfer learning methods that utilized pre-trained models and selecting the best hyperparameters, training the model, and finally testing the created classification model. The research results indicate that the pre-trained MobileNetV2 model with hyperparameter tuning achieved an accuracy of 90%, and the pre-trained DenseNet201 model achieved an accuracy of 93%. The research results indicate that this approach enables the model to achieve excellent performance in recognizing and classifying defective coffee beans with high accuracy
SPARRING: Sistem Rekomendasi Peneliti Terintegrasi Google Scholar via SerpAPI dan Latent Dirichlet Allocation pada Konteks Perguruan Tinggi Ma'ady, Mochamad Nizar Palefi; Rizaldy, Denny Daffa; Satria, Rahul Fahmi; Anaking, Purnama
Jurnal Teknologi dan Manajemen Informatika Vol. 9 No. 2 (2023): Desember 2023
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v9i2.11111

Abstract

The researcher partner recommendation system plays a crucial role in fostering academic collaboration in universities, where a challenge for new users is finding suitable research partners. In addressing the limitations of Naïve Bayes classifiers, this article introduces an innovative approach in the form of a non-linear sigmoid activation function. We highlight the urgency of this solution, detail its implementation steps, and describe its substantial contribution to research partner recommendations. This article not only identifies existing obstacles but also proposes revolutionary solutions to enhance the effectiveness of consultation systems in academic environments. A gap in this research is the manual input method for data retrieval, creating weaknesses, susceptibility to human errors, and reduced efficiency in collecting journal data. We propose SPARRING, a researcher recommendation system connected to Google Scholar, in the context of higher education. This approach uses a dataset of faculty members from the Faculty of Information Technology and Business at a private university in Indonesia. The results from Google Scholar extraction, with topic keywords determined by Latent Dirichlet Allocation, are then classified using the Naïve Bayes algorithm. Additionally, we integrate web scraping tools, particularly SerpAPI, to access data from Google Scholar. Through the integration of SerpAPI, the proposed web-based system is capable of providing more accurate recommendations, especially for new users with limited collaboration experience. By incorporating SerpAPI, the proposed web-based system can offer more accurate recommendations, particularly for new users without extensive collaboration experience.
SIBI (Sistem Bahasa Isyarat Indonesia) berbasis Machine Learning dan Computer Vision untuk Membantu Komunikasi Tuna Rungu dan Tuna Wicara Budiman, Saiful Nur; Lestanti, Sri; Yuana, Haris; Awwalin, Beta Nurul
Jurnal Teknologi dan Manajemen Informatika Vol. 9 No. 2 (2023): Desember 2023
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v9i2.10993

Abstract

The Indonesian Sign Language System (SIBI) is used to translate sign language into text or speech. SIBI helps improve communication between people using sign language and those who do not understand it. Unlike commonly used languages, SIBI sign language is less known to most people due to a lack of interest. To address this, an artificial intelligence-based application was developed, focusing on deep learning to recognize SIBI sign language hand movements in real-time. The model was created with 20 epochs, a batch size of 16, and a learning rate of 0.001. It consists of 13 layers, with the ReLU activation function used for the input layer, while the output layer uses Sigmoid. The ADAM optimizer was used to expedite the model creation process. The image dataset used had a size of 300x300 pixels. In the classification testing of the SIBI alphabet results in this study, it was tested using distance tests. The distance between the webcam and the SIBI language speaker was divided into two categories: 40 cm and 60 cm. For a 40-cm distance, an accuracy of 87.50% was obtained, and for a 60-cm distance, an accuracy of 79.17% was achieved. One limitation of this study is that two alphabets, J and Z, were not included in the dataset. This is because recognition of these two alphabets requires not only finger pattern recognition but also recognition of their gesture patterns.
Analisis Pengaruh Kualitas Sistem, Kualitas Informasi dan Kualitas Layanan Platform Pembelajaran Daring terhadap Kepuasan Pengguna Firdaus, Nur Muhammad; Ardianto, Yusaq Tomo; Sisharini, Nanik
Jurnal Teknologi dan Manajemen Informatika Vol. 10 No. 1 (2024): Juni 2024
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v10i1.12294

Abstract

In the digital age, the shift from sliding learning to online learning has become a must, which forces educational institutions and pupils to adapt to the dynamics of technology. Using the Zoom Meeting online learning system as one of the learning media, it shows how important technology is in organizing learning to be more accessible and flexible. Students can easily attend online lectures or seminars anytime and anywhere. The selection of samples is done using purposive sampling, by selecting samples based on certain criteria that are already defined, namely active students of the Master of Management at Merdeka Malang University who use the Zoom Meeting application as a platform to learn online, as many as 115 respondents. The study collected data by sending questionnaires to respondents through Google Forms. The data was analyzed through three different approaches: descriptive analysis, classical assumptions, and double linear regression. The results showed that the three variables, namely system quality, information quality, and service quality, have an impact on the satisfaction of Zoom users. Among the three, the quality of service was found to have the greatest impact on improving user satisfaction.
Boosting Electronics Manufacturing Efficiency with Automated Data Mining and OEE Process Analytic Sumargo, Ruly; Santoso, Handri
Jurnal Teknologi dan Manajemen Informatika Vol. 10 No. 1 (2024): Juni 2024
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v10i1.11377

Abstract

In the last few decades, the industrial sector has experienced rapid growth, driven by increasing demand and intense competition among manufacturers, especially in the electronics sector. This competition focuses on providing superior products with competitive prices, maintained quality, and optimal delivery times. Optimizing manufacturing processes and effectively utilizing company resources have become key to competitiveness in the manufacturing industry. To ensure comprehensive optimization and smooth manufacturing workflows, it is crucial to engage in systematic evaluation and analytical processes. One of the key performance metrics in assessing manufacturing process efficiency is Overall Equipment Efficiency (OEE), which is used to uncover improvement opportunities and inefficient areas. Accurate OEE measurement requires a data mining systems with automated quantitative data collection methods and real-time calculations. These systems visualize process losses in six (pareto) groups, aiding users in analyzing processes and determining process improvements. The implementation of OEE and alert systems for management can bring an 11.82% increase in overall production efficiency. This achievement can serve as a model for other companies embarking on the initial stages of digital transformation processes.
Implementasi Fungsi Polinomial pada Algoritma Gradient Boosting Regressor: Studi Regresi pada Dataset Obat-Obatan Kadaluarsa Sebagai Material Antikorosi Putranto, Nicholaus Verdhy; Akrom, Muhamad; Trinapradika, Gustina Alfa
Jurnal Teknologi dan Manajemen Informatika Vol. 9 No. 2 (2023): Desember 2023
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v9i2.11192

Abstract

Corrosion is an electrochemical process between the metal surface and a corrosive environment that can lead to significant losses in various industries, especially in the oil and gas sector. Experimental studies are conducted to evaluate the performance of corrosion inhibitors and available resources. In this research, a machine learning (ML) approach is employed to assess the effectiveness of expired drug compounds as corrosion inhibitors. The primary challenge in machine learning is obtaining a highly accurate model to ensure that predictions are relevant to the properties of the tested materials. Therefore, the polynomial function is tested in the gradient-boosting regressor (GBR) algorithm to enhance the accuracy of the developed ML model. The results indicate that the implementation of the polynomial function in the GBR algorithm can improve the accuracy of the prediction model based on R2 and RMSE metrics.
Klasifikasi Jenis Rumah Adat Malaka Menggunakan Metode Convulational Neural Network (CNN) Nahak, Redemtus; Bura, Audyel Umbu; Araujo, Aprilio Demetrius De; Un, Fransiskus Deni; Ladopurab, Bartolomeus Wadan; Marisa, Fitri; Maukar, Anastasia L
Jurnal Teknologi dan Manajemen Informatika Vol. 9 No. 2 (2023): Desember 2023
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v9i2.10352

Abstract

In Indonesia, there is a rich diversity of cultures, one of which is traditional houses. Traditional houses essentially have the potential to represent the way of life, culture, and local economy. Traditional houses in Indonesia, particularly in the Malaka region, are important cultural symbols that can be regarded as cultural icons in Malaka and Indonesia. They provide a historical perspective, heritage, and reflect the progress of society in a civilization. The Convolutional Neural Network (CNN) method is used in this research. In this study, the CNN algorithm is applied to classify traditional house objects. These traditional house objects are divided into two categories: Kolibein Traditional House and Laleik Traditional House. The objective of this research is to classify traditional houses in Malaka, namely Kolibein Traditional House and Laleik Traditional House, and also to determine the accuracy level of CNN classification results. The previously created model is tested using test data to assess its accuracy. The testing is conducted on 20 data points, with 10 data points in each respective class. The testing results show that the classification of Kolibein and Laleik traditional houses is error- free or very accurate. Based on the model developed for classifying Kolibein and Laleik traditional houses using the Convolutional Neural Network method, it is evident that this method is capable of producing accurate results. The obtained results indicate that the accuracy, based on the classification report using images of Kolibein and Laleik traditional houses, reaches 100%. Therefore, it can be concluded that the constructed CNN model has a high level of accuracy.
Designing a Company Risk Register and Risk Monitoring System to Assist in Managing Aircraft MRO Risks Dhamayanti, Raditia; Chudra, Glenny; Yohannis, Alfa
Jurnal Teknologi dan Manajemen Informatika Vol. 10 No. 1 (2024): Juni 2024
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v10i1.12518

Abstract

This company specializes in maintenance, repair, and overhaul (MRO) services for aircraft. MRO activities are crucial to ensuring the safety and reliability of aircraft operations. However, these activities also involve various risks that can affect the performance and reputation of companies in multiple fields. To manage these risks effectively, a system for risk registration and monitoring has been designed for companies in the MRO industry. This paper presents the design of the Risk Register and Monitoring System, which includes the identification, assessment, and management of risks related to the activities of all existing units within companies in the MRO industry. This system is designed to provide a structured approach to risk management, which can help companies in the MRO industry manage risks across all units. The proposed system consists of several components, including automation for performing risk registers and monitoring through a website. This system allows companies in the MRO industry to assess the severity of risks, simplify risk management strategies, and streamline the risk registration process. As a result, companies in the MRO industry can minimize the impact of risks from each unit and enhance efficiency and effectiveness in risk management
Forecasting Model of Indonesia's Oil & Gas and Non-Oil & Gas Export Value using Var and LSTM Methods Rijal, Khaidar Ahsanur; Vitianingsih, Anik Vega; Kristyawan, Yudi; Maukar, Anastasia Lidya; Wati, Seftin Fitri Ana
Jurnal Teknologi dan Manajemen Informatika Vol. 10 No. 1 (2024): Juni 2024
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v10i1.13127

Abstract

As a country with abundant natural resources in the form of mineral and non-mineral products, Indonesia is characterized by its ability to fulfill domestic and foreign needs through export activities categorized into two commodities: oil and gas and non-oil and gas. Export activities are an indicator of the country's economic growth that often fluctuates in value, and these conditions are fundamentally caused by a decrease in production quantity and the instability of the global economic climate. The strategy to overcome these problems is to create a forecasting model. This research aims to develop a forecasting model using time series analysis methods, including vector autoregressive (VAR) and long short-term memory (LSTM) methods based on oil and non-oil and gas value parameters. The results of the Granger causality test stated that the values of oil and gas and non-oil and gas affect each other. The VAR model with the optimum lag produced by the Akaike Information Criterion (AIC) test obtained an accuracy value of MAPE oil & gas and non-oil and gas of 18.4% and 32.1%, respectively. LSTM generates the best model with a MAPE value of 6,23% for oil & gas and 8,18% for non-oil and gas.
Pemanfaatan Mediapipe Body Pose Estimation dan Dynamic Time Warping untuk Pembelajaran Tari Remo Effendi, Yusuf; Kristian, Yosi; P.C.S.W, Lukman Zaman; Yutanto, Hariadi
Jurnal Teknologi dan Manajemen Informatika Vol. 9 No. 2 (2023): Desember 2023
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v9i2.10408

Abstract

Video can be used as a learning medium for various purposes. In this research, the object of study is the movements of the traditional dance "remo." Thus, as a substitute for an absent coach or instructor, videos can take on the role of a dance instructor. However, video communication is one-way between the coach and the learners. Without movement correction, individuals trying to learn remo dance may find it challenging to determine if they are performing the movements correctly. Therefore, the author aims to create a system to assist coaches in correcting the dance movements of their learners. Using the MediaPipe module and the Dynamic Time Warping algorithm, the author developed a system to correct the learners' dance movements. This system can detect deviations from the coach's instructional video and provide notifications about which body angles do not match the coach's video instructions. The system operates by having users upload a video of their dance movements, and then it identifies which movements deviate from the correct remo dance. The accuracy is measured by comparing the angle distances between the master's movements and the test data. If the angle exceeds a predetermined threshold, the movement is considered incorrect. The system's output is validated by the coach, and it achieves 90% accuracy in identifying movement errors in videos. With this accuracy, the system can assist coaches in evaluating their learners' remo dance movements.

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