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INDONESIA
JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
ISSN : -     EISSN : 2686228X     DOI : -
Core Subject : Science,
Artikel yang dimuat melalui proses Blind Review oleh Jurnal JOSH, dengan mempertimbangkan antara lain: terpenuhinya persyaratan baku publikasi jurnal, metodologi riset yang digunakan, dan signifikansi kontribusi hasil riset terhadap pengembangan keilmuan bidang teknologi dan informasi. Fokus Journal of Information System Research (JOSH)
Articles 795 Documents
Implementasi Sistem Pengambilan Nomor Antrean Online dengan Pendekatan Waterfall dan Keamanan MFA Bleskadit, Adri Agustinus; Dewi, Christine
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6187

Abstract

In the digital era, information technology plays an important role in increasing the efficiency of various sectors, including public services. One of the problems faced by the XYZ office in tax services is taking queue numbers. Long queues often cause long waiting times for visitors and reduce company efficiency, which ultimately impacts public satisfaction and perceptions of public services. An efficient queuing system not only improves the user experience but also the productivity of the institution. However, manual systems are often slow, prone to errors, and less flexible, so digital-based solutions are needed. This research aims to design a website-based queue number retrieval system using the waterfall method. To ensure the security of user data, the system is equipped with a Multi-Factor Authentication (MFA) feature, which increases the protection of user data from unauthorized access. This system was built using the PHP programming language and is supported by the XAMPP device as a local server. Tools such as Entity Relationship Diagrams (ERD) and Unified Modeling Language (UML) are used to design data structures and system flows effectively. It is hoped that this research will provide a practical solution to make it easier to collect queue numbers online, reduce waiting times, and increase user satisfaction and safety at the XYZ office.
Prediksi Spasial Kerapatan Vegetasi Perkotaan dengan Pendekatan Algoritma Time Series Untuk Mendukung Pertumbuhan Ekonomi Hijau Pratama, Yudistira Bagus; Dalimunthe, Nurzaidah Putri; Sukma, Mega
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6251

Abstract

The urgency of this research is based on data from the Pangkalpinang City Population and Civil Registration Service in 2020, the population reached 218,569 people and continued to grow to 232,915 people in 2023. The importance of monitoring vegetation density in the context of green economic growth, which requires careful evaluation of the balance between economic development and environmental conservation. With rapid urban growth, the Pangkalpinang city government requires a variety of approaches to accurately predict vegetation density. By identifying the factors that affect land vegetation density, this study aims to develop a machine learning model that can predict land vegetation density conditions over a certain period of time in the future. This research method involves collecting spatial vegetation density data over a period of 11 years using remote sensing technology or remote monitoring, such as satellite imagery. Furthermore, time series data will be analyzed and modeled using machine learning techniques, focusing on algorithms that can overcome the spatial and temporal dynamics of vegetation density. Machine learning algorithms, especially time series algorithms such as Autoregressive Integrated Moving Average (ARIMA) will be used to build a spatial prediction model for vegetation density. The results of this study indicate that the use of ARIMA is able to produce an accurate prediction model in projecting vegetation density in Pangkalpinang City. The ARIMA model shows strong performance with low error metrics, indicating its effectiveness in making accurate predictions for the given data set. The results of this study are expected to provide valuable information for the Pangkalpinang city government in making decisions related to environmental management and green economic development. By involving collaboration between researchers with three complementary expertise including computer science, civil engineering and natural resource conservation and policy makers.
A Qualitative Study on Justice and Fairness Perception Through Kohlberg's Theory using Video Games Wibowo, Tony; Deli, Deli; Hanita, Hanita
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6666

Abstract

This study explores how narrative-driven video games can enhance adolescents' moral reasoning, focusing on justice and fairness through Kohlberg’s moral development theory. Addressing the challenge of fostering advanced moral reasoning in youth, it highlights the limitations of traditional methods and the potential of video games to immerse players in ethical dilemmas. Using a qualitative approach, 35 adolescents played a story-based video game and participated in interviews and group discussions. Thematic analysis revealed that 60% began with pre-conventional reasoning, emphasizing individual rewards, but many advanced to conventional reasoning (Stage 4) after gameplay. A smaller group (15%) demonstrated post-conventional reasoning (Stage 5), considering fairness and abstract principles. While 40% found moral options confusing, 11.43% formed emotional connections with the narrative, underscoring the role of storytelling in fostering empathy and reflection. The findings suggest that thoughtfully designed video games can bridge gaps in moral education, offering engaging contexts for ethical exploration. This research supports integrating such games into curricula to enhance moral and cognitive growth in adolescents.
Sistem Otentifikasi Otomatis Kendala Perangkat Jaringan Menggunakan NDLC Hadi, Muhammad Fawazi; Vidiasari, Viviana Herlita; Lauwl, Christoper Michael; Husain, Husain; Amin, Farda Milanda
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6685

Abstract

The authentication system is a monitoring system where if there is a network problem, it can provide information quickly. The research was conducted at Bumigora University, using 3 buildings as research materials implemented in the form of topology design. The monitoring is carried out to anticipate lost connections or network disconnections caused by certain factors. The Network Development Life Cycle (NDLC) method is the appropriate method for conducting this analysis, because it focuses on parameters such as network reliability and the ability to secure data transmission. The stages of the NDLC method consist of analysis, design, simulation prototype, implementation, monitoring and management, but in this study the author only used 3 stages, namely analysis, design, simulation prototype and implementation. The NDLC method has been proven to increase network security and reliability, as well as minimize downtime due to device failure or disconnection of data transmission. Automatic authentication implemented through NDLC allows real-time device monitoring, by connecting the API with telegram. Telegram will provide notifications in the form of network condition statuses that are experiencing problems. So that the IT team can control the condition of network devices through telegram notifications. This can facilitate network management and efficiency of checking time in maintaining the stability and performance of the network as a whole.
Penerapan Metode Monte Carlo dalam Memprediksi Suhu Daerah Perkotaan Marpaung, Tulus Joseph; Marpaung, Rony Genevent
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6693

Abstract

Changes in temperature patterns due to climate change are a global challenge that requires in-depth analysis, especially in tropical regions such as the city of Medan, Indonesia. This research aims to project future temperature patterns using the Monte Carlo simulation method, utilizing historical data on daily average temperatures from the Meteorology, Climatology and Geophysics Agency (BMKG). A probability-based Monte Carlo method is used to analyze the future temperature distribution, applying the normal distribution as the basic model. Parameters such as mean and standard deviation are calculated accurately, and thousands of iterations are performed to ensure stable and representative simulation results. The analysis process is carried out using Python and supporting libraries such as NumPy, SciPy, and Matplotlib, which provide flexibility and efficiency in environmental data processing. The results of this study show that the Monte Carlo method can produce future temperature distributions that reflect daily temperature variations as well as the probability of extreme events. These predictions provide important insights for various sectors, including health, energy and urban planning, in developing strategic plans to deal with the impacts of climate change. This research confirms that Monte Carlo simulation is an effective approach for analyzing climate data in tropical regions. Additionally, this research opens up opportunities for further development, such as integrating additional data and adapting the model to different environmental scenarios to improve prediction accuracy and relevance of results.
Kajian Metode Analisis Spektral Pada Peramalan Curah Hujan Zega, Putri May Sari; Mardiningsih, Mardiningsih
Journal of Information System Research (JOSH) Vol 6 No 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i3.6694

Abstract

Rainfall is an important element in climate research, and its analysis requires appropriate approaches to reveal seasonal patterns and periodicity. This research aims to explore the application of the spectral analysis method in rainfall forecasting using monthly rainfall data from Serdang Bedagai Regency for 10 years (January 2014 – December 2023) totaling 120 data. The method used is harmonic analysis, a Fourier approach to extracting frequency information from time series data. This research began with data visualization, stationarity testing using the Phillips-Perron test, to spectral analysis involving calculations of Fourier coefficients and periodograms to detect dominant frequencies. The results show that the data has a periodic component with the highest frequency at 0.524, which is equivalent to a 12 month period. These results indicate the existence of seasonal patterns in rainfall data, which is relevant to support more accurate climate forecasting models. The implications of this research include the use of spectral analysis as a reliable method for identifying periodicity and building seasonal pattern-based forecasting models, which can be applied in various studies related to climate change and its mitigation.
Perancangan dan Implementasi Sistem Keamanan Pada Brankas Menggunakan Paralel Fingerprint dan Keypad Berbasis Arduino Al Rafif, Muhammad Roid; Paniran, Paniran; Wiriasto, Giri Wahyu
Journal of Information System Research (JOSH) Vol 6 No 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i3.6787

Abstract

Safe security plays an important role in protecting valuable assets from unauthorized access. Designing and implementing a security system on a safe using a parallel combination of two fingerprint sensors and a keypad. This is to increase the level of security by ensuring that both fingerprint sensors verify the fingerprint simultaneously before the user enters the code via the keypad to open the safe. The system controller uses an Arduino Mega as the main microcontroller, which connects two fingerprint sensors, a 4x4 keypad, a 16x2 LCD to display information, a 1 channel relay to drive the solenoid door lock as an actuator, and a buzzer as a warning indicator. The system testing scenario consists of three stages, namely, Electronic relay testing is carried out to ensure that the relay functions properly in controlling the solenoid door lock, so that the locking and opening mechanisms of the safe can run according to command. Testing on parallel fingerprints aims to evaluate the accuracy and speed of the sensor in reading two fingerprints simultaneously and ensuring that only registered fingerprints can access the system. Furthermore, keypad testing is carried out to verify the accuracy in reading the access code and testing the warning system in the event of an input error or unauthorized access attempt. The implementation results show that the system has a fingerprint authentication success rate of 98%, with an average response time of 1.2 seconds. The keypad reading accuracy reaches 99%, while the safe locking and opening mechanism functions with a 100% success rate. With these results, the developed system is proven to work reliably and provide additional protection compared to conventional methods.
Aplikasi Pengelola Keuangan Pribadi Berbasis Android dengan Pendekatan User Centered Design (UCD) Triwidadi, Kurniawan Adhisukma; Widodo, Tri
Journal of Information System Research (JOSH) Vol 6 No 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i3.6841

Abstract

Financial management plays a very important role in everyday life. Proper management can help one in organizing and controlling expenses. In the process of financial management, people should prioritize basic needs first. However, it is often difficult to distinguish between primary, secondary, and additional needs, which ultimately risks creating a consumptive culture, which is the habit of buying things that are not really needed. One solution to overcome this is to design an application that can help manage finances, equipped with features that limit spending if it exceeds the balance of income or if non-primary expenses exceed 20% of total income. In this research, the approach used is User Centered Design (UCD) which focuses on meeting the needs of users in using the application. The application was also tested using the Black Box method and obtained a 100% success percentage. The features of classifying and recording needs greatly facilitate users in managing finances. The results of the study can provide benefits for users in managing finances, especially in managing primary needs, so that they can manage finances more effectively.
Analisis Perbandingan Metode Random Forest dan Adaptive Boosting Untuk Prediksi Leukemia dengan Data Microarray Heremba, Juleha Irianti; Suhendra, Christian Dwi; Sanglise, Marlinda
Journal of Information System Research (JOSH) Vol 6 No 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i3.6898

Abstract

Cancer is the uncontrolled growth of cells that spread to other parts of the body. There are different types of cancer that are named after the organ they originate from. One of them is blood cancer or leukemia, which is bone marrow cancer caused by genetic mutations. According to data from Global Cancer Statistics in 2020, there were an estimated 19.3 million new cancer cases and 10 million cancer deaths, and it is estimated that by 2040 it will increase globally by 47% from 19.3 million to 28.4 million new cancer cases. Leukemia is one type of cancer with the ninth rank in Indonesia in 2020, there are 14,979 new cases and 11,530 cases of death caused by leukemia. One of the efforts to prevent leukemia can be done by diagnosing the acute leukemia category using DNA and genetic information. The purpose of this study is to analyze the comparative performance between Random Forest and Adaptive Boosting methods in predicting leukemia types using microarray datasets to determine which method is more effective in performing classification. In this study, the dataset used is gene expression in bone marrow and blood consisting of two categories of acute leukemia, namely Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL) obtained with DNA microarray technology. These genes will be classified using Random Forest and Adaboost methods to predict acute leukemia categories. The results of the analysis process show that the random forest method is a better method for predicting acute leukemia with an Area Under Curve value of 100%, Accuracy 92.9%, Precision 93.7%, Recall 92.9%, and F1-Score 92.7% compared to the AdaBoost method with an Area Under Curve value of 83.3%, Accuracy 85.7%, Precision 88.6%, Recall 85.7%, and F1-Score 85.1%.
Klasifikasi Siswa Slow Learner Menggunakan Algoritma C4.5 Dalam Optimalisasi Pembelajaran di Sekolah Menengah Pertama Gloria, Bela Priska; Hidayati, Rahmi; Hasfani, Hirzen
Journal of Information System Research (JOSH) Vol 6 No 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i3.6932

Abstract

Education is a learning process aimed at enhancing students' abilities in the school environment. SMP Negeri 12 Sungai Ambawang is one of the educational institutions located in Sungai Ambawang District. In the learning process, each student has a different level of understanding of the material being taught. Some students struggle to grasp lessons at the same pace as their peers, which categorizes them as slow learners. Lack of awareness about slow learners can hinder the teaching and learning process, as teachers must repeatedly explain the material. Therefore, this study aims to identify and classify slow learners using the C4.5 machine learning algorithm to help schools design more effective and adaptive learning strategies. The classification of slow learners is divided into four categories: normal, mild, moderate, and severe. The dataset consists of 135 data points, including 81 training samples and 54 testing samples. The attributes used include scores from subjects such as Civics (PKN), Indonesian Language, Mathematics, Natural Sciences (IPA), Social Sciences (IPS), and English. The C4.5 algorithm generates a decision tree with Natural Sciences (IPA) as the root node attribute. Testing using the Confusion Matrix shows an accuracy of 91%, precision of 54%, recall of 68%, and an error rate of 9%. The classification results indicate that 9% of students fall into the normal category, 24% into the mild category, 62% into the moderate category, and 0% into the severe category. These results demonstrate that the C4.5 algorithm is effective in classifying slow learners.