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Jurnal Pilar Nusa Mandiri

pilar
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Published by STMIK Nusa Mandiri

ISSN : 19781946 EISSN : 25276514 DOI : -

Core Subject : Science,

Computer Science & IT

Jurnal Pilar merupakan jurnal ilmiah yang diterbitkan oleh program studi sistem informasi STMIK Nusa Mandiri. Jurnal ini berisi tentang karya ilmiah yang bertemakan: Rekayasa Perangkat Lunak, Sistem Pakar, Sistem Penunjang, Keputusan, Perancangan Sistem Informasi, Data Mining, Pengolahan Citra.

Arjuna Subject : -

Articles 418 Documents

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DESIGN AND DEVELOPMENT OF AN INTERNAL QUALITY AUDIT INFORMATION SYSTEM BASED PPEPP CYCLE Yani, Ahmad; Bakti, Lalu Darmawan; Akbar, Ardiyallah; Imran, Bahtiar
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.4729

The Mataram University of Technology Quality Assurance Institute already has and has established national education standards plus the standards set by universities following Permendikbud number 3 of 2020. However, there are problems with the implementation of Internal Quality Audits, where the implementation of internal quality audits is very less effective and efficient, good in terms of time, cost, and energy. This is because the Mataram University of Technology Quality Assurance Institute only has 3 auditors to audit 12 study programs in one year and even spends two months in a row. This is an important concern for researchers to build and produce an internal quality audit information system application program that can help implement the internal quality audit process carried out by the Mataram University of Technology Quality Assurance Institute. The design of the internal quality audit information system was carried out using the prototyping method. The application of the prototyping method in system design will make information system builders better and more structured. The internal quality audit information system was built using the PHP programming language with the CodeIgniter framework and MySQL as the database and implementing Code-View-Controller (MVC). The main objective of this research is to produce an internal quality audit information system so that it can assist the Mataram University of Technology Quality Assurance Institute in documenting and optimizing higher education quality management in a planned and sustainable manner following the PPEPP cycle

CLUSTERING OF POPULAR SPOTIFY SONGS IN 2023 USING K-MEANS METHOD AND SILHOUETTE COEFFICIENT Rohman, Nur; Wibowo, Arief
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.4937

The rapid advancement of technology and globalization in this era has brought about comprehensive and easily accessible music streaming services, one of which is Spotify. According to Kompas.com, Spotify has experienced a rise in subscribers up to 130 million, as a platform that offers various features besides music streaming. Spotify also provides a better user experience and has the ability to compete with other music streaming platforms. The mission of this research is to classify popular Spotify song data in 2023, which can aid in a deeper understanding of listener preferences or music trends. Based on the test results, there were 2 clusters obtained with cluster 0 containing 863 data and cluster 1 containing 90 data. From the testing results conducted in the K-Means analysis, a Silhouette Coefficient of 0.81 was obtained, which falls into the category of Strong Structure. From these results, it can be suggested that cluster formation was done very well to provide more personalized and relevant music recommendations to Spotify platform users. By understanding the preferences and patterns of listeners revealed through clustering, streaming services can enhance user experience by providing more tailored content.

ABILITY CONVOLUTIONAL FEATURE EXTRACTION FOR CHILI LEAF DISEASE USING SUPPORT VECTOR MACHINE CLASSIFICATION Saputra, Rizal Amegia; Haryanto, Toto; Wasyianti, Sri
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.4961

Chili plants are among the most commonly used food ingredients in various dishes in Indonesia. Leaves on chili plants are often affected by disease; if the disease is not treated immediately, it can damage the plant and cause crop failure. Early detection of chili plant diseases is important to reduce the risk of crop failure. The development of technology and the application of machine-learning algorithms can automatically monitor chili plants using a computer system. Using this algorithm, the system analyzes and identifies diseases that a camera can observe and record. In this study, the proposed method for feature extraction uses a convolutional neural network (CNN) algorithm with transfer learning using VGG19. For classification using SVM for training data, accuracy generated 95%, precision 95%, recall 95%, and F1-Score 95%, and testing data accuracy generated 90%, precision 89%, recall 90%, and F1-Score 89%, proving that the convolutional process with architecture VGG19 and SVM algorithm is acceptable for classification. In future research, other architectures or extraction fusions can be used to maximize the results.

IMPLEMENTATION OF ARMA MODEL FOR BENGAWAN SOLO RIVER WATER LEVEL AT JURUG MONITORING POST Siswanti, Sri; Vulandari, Retno Tri; Setiyowati, Setiyowati
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.5004

The amount of annual rainfall in the Bengawan Solo watershed causes high water flow (water discharge) in several rivers. In addition, high flow rates significantly increased the water surface level at some observation sites. The Bengawan Solo River burst its banks in November 2016, causing flooding in several areas in eastern Solo. At that time, the river stage at the Jurug monitoring post passed ten. Therefore, a flood early warning system would be useful for predicting water levels in this context. Every day, one post on the Bengawan Solo River measures the water level. The time series data used in this study is the water level. Autoregressive Moving Average (ARMA) is a predictive method for measuring time set data. The assumption of homoscedasticity or constant error variance is used in this model. However, the study will use the ARMA model if the variance changes randomly. The study used 60 pieces of data from January to February 2018. This study can directly use ARMA because the data results are stationary based on ADF value 0.0036. After the first pause, the ACF and PACF are disconnected based on the correlogram pattern. This shows that the water level of the Bengawan Solo River in that period can appear on the Autoregressive Moving Average with orders p = 1 and q = 1 ARMA(1,1). Thus, the total average residue is 0.0668384, so the short error is 6.68384%.

DEEP LEARNING FOR AUTOMATIC CLASSIFICATION OF AVOCADO FRUIT MATURITY Widiati, Wina; Haryanto, Toto
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.5043

Avocado (Persea Americana), a fleshy fruit with a single seed, has increased in popularity globally, especially in tropical and Mediterranean climates, thanks to its commercial and nutritional value. Rich in bioactive compounds, avocados contribute to the prevention and treatment of various diseases, including cardiovascular problems and cancer. Avocado production in Indonesia, for example, is showing a significant increase, reflecting the growing demand. Avocado ripeness affects shelf life and quality, making the determination of ripeness level a critical aspect of postharvest management. Skin color and pulp firmness change during storage, affecting quality and nutritional value. Proper classification of ripeness is important to reduce post-harvest losses, improve quality and optimize export costs. Recent research shows the use of technologies such as machine learning and YOLO (You Only Look Once) version 9 in real-time detection of avocado ripeness, offering innovative solutions to reduce post-harvest losses and improve distribution efficiency. This approach not only benefits farmers and consumers but also ensures consumer satisfaction and reduces economic losses. This study highlights the importance of real-time detection in monitoring avocado ripeness, where the training process was conducted for 89,280 iterations resulting in a new model for avocado ripeness detection. The final model has a mean Average Precision (mAP) validation value of 84.3%, mAP 84.3% signifies the optimal level of accuracy in object recognition in avocado fruit maturity images using the YOLO model that has undergone an intensive training process.

EXPLORING THE ECONOMIC IMPACT OF SMART CITY INVESTMENT: A LITERATURE REVIEW Febiyanti, Widyantari; Subriadi, Apol Pribadi
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5047

The current implementation of smart cities aims to enhance the quality of life for their communities. Smart city investments can be achieved through collaborative efforts between information and communication technology and human resources, transforming areas into sustainable cities. Many countries are becoming increasingly interested in smart city investments. However, it remains unclear how these investments can achieve their intended goals. The primary issue lies in the lack of effective methodologies to measure the economic impact of such investments. Many cities lack comprehensive assessment tools to gauge the economic impact of smart city implementations, making it difficult to determine whether these investments deliver the desired benefits. This article aims to provide references regarding the economic impacts of smart city investments and the frameworks that can be used to measure them. The methodology employed in this research is a literature review based on references published over the past five years. According to findings, smart city investments have been found to impact aspects such as e-commerce and e-business, the creation of environmentally friendly environments, cost savings and economic benefits, GDP growth, and increased income for regions/cities through effective smart city utilization. Several frameworks have been gathered to measure economic impact, such as Computable General Equilibrium (CGE), Energy Efficient Integrated Planning Framework (EEIPF), and Open Data Impact for Smart Cities Framework (ODISC). Each framework serves to illustrate how examples of smart city investments can influence the economy of a region or city.

DEVELOPMENT OF CINEVERSE FILM WEBSITE UTILIZING THEMOVIEDB'S API FOR DYNAMIC CONTENT MANAGEMENT Fadhilah, Siti Nur; Utomo, Fandy Setyo
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.5210

The development of websites in this digital era is crucial to creating captivating and relevant online experiences. The combination of server-side programming and client-side technologies along with MySQL database management forms the foundation for a dynamic user interface emphasizes the significance of integrating various technologies to achieve this goal. This project involves the use of PHP, HTML, CSS, JavaScript, and MySQL, with the integration of The Movie Database (TMDB) API, showcasing the intricate fusion of creativity, technical prowess, and data integration. The resulting website offers a comprehensive list of films with detailed information and posters, enhancing the user experience and making it an essential read for those interested in crafting immersive online experiences. The abstract of this research aims to explore the process of website development using diverse technologies and data integration and to analyze its impact on user experience. By examining aspects such as security, performance, and routine maintenance, this study aims to provide in-depth insights into producing captivating and relevant online experiences in the context of modern web development.

CLASSIFICATION OF CUSTOMERS’ REPEAT ORDER PROBABILITY USING DECISION TREE, NAÏVE BAYES AND RANDOM FOREST Dewi, Amelia Citra; Hermawan, Arief; Avianto, Donny
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.5243

Limited customer information in sales data on e-commerce in Indonesia hinders companies in determining targeted marketing strategies, especially in targeting groups of potential customers to make repeat purchases. Sales data in the form of customers' names and cellphone numbers has been hidden by e-commerce, and only data is available in the form of products purchased, number of purchases, and customer addresses. So far, the methods used to determine potential customers mostly use more complete data features. Research that uses limited e-commerce data to determine potential customers is scarce. Several algorithms for predicting repeat purchases in e-commerce also have been widely used. However, the comparison of the performance of these methods in the context of e-commerce in Indonesia with limited data has yet to be discovered. In this research, the Decision Tree, Naive Bayes, and Random Forest methods were compared to classify potential customers using Maschere brand sales data from two e-commerce sites, namely Tokopedia and Shopee. The research results show that the Decision Tree algorithm achieved an accuracy of 90.91%, Naive Bayes achieved an accuracy of 37.50%, and Random Forest achieved the best level of accuracy, namely 93.94%. These results show that the Random Forest method is the best method for classifying customers' probability of repeat purchases. In the future, the results of this research can be developed again as a decision-making system to determine potential customers.

IDENTIFICATION OF POTATO LEAF DISEASES USING ARTIFICIAL NEURAL NETWORKS WITH EXTREME LEARNING MACHINE ALGORITHM Erkamim, Moh.; Septarini, Ri Sabti; Tonggiroh, Mursalim; Nurhayati, Siti
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.5307

Potato plants have an important role in providing a source of carbohydrates for society. However, potato production is often threatened by various plant diseases, such as leaf disease, which can cause a decrease in yields. Identification of diseases on potato leaves is currently mostly done by farmers manually, so it is not always efficient and accurate. So the aim of this research is to identify diseases on potato leaves with artificial neural networks using the ELM (Extreme Learning Machine) approach and the GLCM (Gray Level Co-Occurrence Matrix) method for feature extraction. The GLCM approach functions to obtain texture features on objects by measuring how often certain pairs of pixel intensities appear together at various distances and directions in the image. Meanwhile, the ELM algorithm is used for image identification by adopting a one-time training method without iteration, which involves randomly determining weights and biases in hidden layers, thus allowing training to be carried out quickly and efficiently. Evaluation of the model by looking for the level of accuracy produces a value of 84.667%. The results show that the model developed is capable of accurate identification.

DEVELOPMENT OF FIELD WORK PRACTICE MANAGEMENT IN INFORMATICS INTEGRATED SERVICE SYSTEM Astuti, Indah Fitri; Hendi, Hendi; Cahyadi, Dedy; Kridalaksana, Awang Harsa
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5523

Field work practice is crucial for aligning college education with workplace skills, but manual management poses challenges such as time-consuming processes and limited information access, particularly within the Informatics Program at Mulawarman University. This research explores integrating field work practice with mobile technology through Rapid Application Development, resulting in a comprehensive field work practice management mobile app. Data collected from direct observation and student questionnaires inform the app's development. The app covers various field work practice aspects, facilitating efficient student engagement, and Black Box testing confirms its functionality without errors. This research significantly contributes to field work practice management, offering stakeholders an efficient, transparent, and well-integrated solution for field work practice implementation in academic settings, ultimately aiming to enhance convenience and accelerate the field work practice process for all involved parties.

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