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Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Published by Universitas Pakuan
ISSN : 16937554     EISSN : 26543990     DOI : 10.33751
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Scientific Journal of Computer and Mathematical Science (Jurnal Ilmiah Ilmu Komputer dan Matematika) is initiated and organized by Department of Computer Science, Faculty of Mathematics and Science, Pakuan University (Unpak), Bogor, Indonesia to accommodate the writing of research results for the academics and institutions other. Komputasi journal was originally launched in 1992, and published online since 2007 with ISSN version p-ISSN: 1693-7554 and version of the daring of e-ISSN: 2654-3990 in 2018 (SK No. 0005.26543990/JI.3.1/SK.ISSN/2018.10-15 October 2018 (starting Vol. 16, No. 1, January 2019). The journal is a publication media for original manuscripsts related information technology development and science written in Bahasa Indonesia which is published twice times a year (January and July).
Arjuna Subject : -
Articles 217 Documents
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Analysis the Level of Experience of Technology Users’ Perspectives on LinkedIn Websites Ibrahim, Aghi Kalam; Supriyatin, Wahyu; Rianto, Yasman
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 2 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i2.9998

Abstract

LinkedIn is a web-based application that can be used by job seekers, both new users and professional users. LinkedIn website is not only used by job seekers but can be used for various other activities. Application user experience is a major assessment of the quality of the software. LinkedIn website user experience can be seen by measuring the website using several aspects. This research aims to analyze the website user experience using the User Experience Questionnaire (UEQ) method. Measurement using the UEQ method is seen by using six aspects of the measurement scale, namely attractiveness, clarity, efficiency, accuracy, stimulation and novelty. Data collection in the study was carried out using a questionnaire given to 41 respondents totaling 26 questions. The questionnaire data will be processed using UEQ Data Analysis Tools. The results of UEQ measurements with benchmark comparisons show four aspects that are in the below average category, namely attractiveness, efficiency, stimulation and novelty. While two aspects are in the bad category, namely clarity and accuracy. So it is necessary to develop and improve the LinkedIn website by developers related to aspects that are in the bad category. The two bad aspects have a value of 0.58 for the clarity aspect and 0.53 for the accuracy aspect.
Linear Kernel Optimization of Support Vector Machine Algorithm on Online Marketplace Sentiment Analysis Andrianto, Fiki; Fadlil, Abdul; Riadi, Imam
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 1 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i1.9266

Abstract

Twitter is a short message platform commonly used as a means of news information, commentary, and social interaction. One of the utilization of twitter is to analyze the sentiment of the online marketplace which can be used to determine the service, quality of goods, and delivery of goods on a product, service or application. This research aims to categorize the reviews or responses of the Indonesian people, especially to the online marketplace using the linear Support Vector Machine (SVM) algorithm. In order to make continuous improvements to the role of the Indonesian online marketplace in the future, sentiment analysis is needed. The analysis research tweets used were 4165 datasets using the python programming language. Sentiment analysis research stages include data collection, preprocessing, labeling, tf-idf weighting, split data, SVM model analysis and result evaluation. The data is then divided into 80% training data and 20% testing data, 50% training data and 50% testing data, 20% training data and 80% testing data. The results of the svm algorithm testing scenario obtained the highest optimization with an accuracy value of 97%, F1-score value on positive labels 88% and negative 98%, also obtained a positive recall value of 80% and negative 100% precision value on positive labels 98% and negative 97%, on 80% training data and 20% testing. It can be concluded that in this case, the linear svm algorithm is able to work to recognize models with a high level of accuracy so that in the future it can be used in similar cases.
Visualization of Guimaras State University Community Extension Services: Basis for Expansion J. Forca, Adrian; Karlitasari, Lita; Putra, Gustian Rama
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 2 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i2.10111

Abstract

The Study Visualization of Guimaras State University Community Extension Services: Basis for Expansion was conducted to visualize the dataset of the GSU Extension Services office to serve as basis for the unit to expand its scope towards the number of communities to be adapted, number of training to be conducted, number of communities adapted vs number of communities that are not adapted and the number of programs implemented in every community cluster. To visualize such, the researcher made use of Tableau Public in which, a descriptive component was used in the study to determine the effectiveness of the visualized information. Visualization was conducted and respondents of the study agreed that the visualized information is effective that can serve as the basis for the GSU Extension Services to expand its present services so that more communities to be adapted and uplift their quality of life.
Message Encryption in Digital Images using the Zhang LSB Imange Method Saepulrohman, Asep; Ismangil, Agus; Heliawati, Leny
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 1 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i1.9314

Abstract

Message encryption in digital images using the Zhang LSB Image method is a steganography technique that utilizes the Least Significant Bit (LSB) method to hide secret messages in the last bit of the image pixel. This method allows the use of images as a medium to convey hidden messages. The encryption process involves two main stages, namely message encryption and message hiding in an image. Message encryption is carried out using strong cryptographic algorithms to secure the authenticity and confidentiality of messages. Then, the encrypted message is inserted into the last bit of the image pixel using the LSB method. This is done by modifying the last bit value of the pixel so that the change is not visually visible to the human eye. To recover the original message, the message recovery process involves extracting the last bit of the modified image pixel and decrypting the message using the appropriate key. The Zhang LSB Image method is a steganography technique that is relatively simple but effective in hiding messages in digital images.
Classification of Heart Disease Diagnoses Using Gaussian Naïve Bayes Akil, Ibnu; Chaidir, Indra
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 2 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i2.10114

Abstract

Machine learning, which is part of artificial intelligence, has been widely applied in various fields, especially the medical field. Machine learning helps doctors make more accurate diagnoses. Heart disease is one of the highest causes of death in the world, so the need for accurate diagnosis is absolute for this disease. There are many algorithms that have been applied in machine learning to classify and detect heart disease, such as Linear Discriminant Analysis [1], KNN, Decision Tree, Random Forest [2], and Logistic Regression [3]. One classification algorithm that has not been implemented is Gaussian Naive Bayes. So, in this research the Gaussian Naive Bayes algorithm will be tested on the cardio health risk assessment dataset. From the research results of applying the Gaussian Naive Bayes algorithm to cardio health risk assessment data, accuracy was 0.87%, precision was 0.88%, recall was 0.90%, and f1-score was 0.89%.
Chaos CSPRNG Design As a Key in Symmetric Cryptography Using Logarithmic Functions Nathanael, Hizkia; Wowor, Alz Danny
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 1 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i1.9265

Abstract

 This research uses the logarithm function as a key component in generating random numbers in the Chaos CSPRNG framework. The main problem addressed here is the generation of keys for cryptography, recognizing the important role of cryptographic keys in safeguarding sensitive information. By using mathematical functions, specifically logarithmic functions, as a key generation method, this research explores the potential for increasing the uncertainty and strength of cryptographic keys.The proposed approach involves the systematic utilization of various mathematical functions to generate diverse and unpredictable data sets. This data set, derived from the application of logarithmic functions, serves as the basis for generating random numbers. Through a series of tests such as Randomness Test and Cryptography Test, this research shows that the data generated from these functions can be utilized effectively as a reliable source for generating random numbers, and has a low correlation value, thereby contributing to the overall security of a symmetric cryptographic system.
Implementation of the Simple Additive Weighting Method in Determining Favorite Lecturers Based on Student Preferences Wahidin, Ahmad Jurnaidi; Ratnasari, Ratnasari; Setiawan, Agustinus Eko
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 2 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i2.9854

Abstract

This study aims to develop a decision support system for evaluating and ranking favorite lecturers based on student preferences in higher education institutions. In the context of modern education, the role of lecturers goes beyond delivering lectures; they also serve as mentors and inspirations for students. However, the process of evaluating lecturer performance is often subjective and lacks structure, especially when it involves student preferences and perceptions of lecturers. Therefore, this study proposes the Simple Additive Weighting (SAW) method as a solution to this problem. By collecting data through Likert scale-based questionnaires, this research evaluates lecturer performance based on four main aspects: Pedagogical, Professionalism, Personality, and Social Interaction. Based on the calculation results using SAW, it was found that the best alternative is Alternative A2, which scored the highest total with 0.997. This indicates that lecturers associated with this alternative received high ratings in all aspects assessed by students. This conclusion provides a clear insight for educational institutions to improve educational management and enrich student learning experiences.
Recommender Systems using Hybrid Demographic and Content-Based Filtering methods for UMKM Products Nadira Putri, Salsa; Awaliyah Zuraiyah, Tjut; Munggaran Akhmad, Dinar
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 1 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i1.8991

Abstract

Marketing digitization such as e-commerce is needed by micro, small and medium enterprises (UMKM) in Bogor City and Regency so that the products are more easily accessible to consumers. One of the digital marketing that is commonly used by consumers is an e-commerce website. The Recommendation System is implemented into e-commerce websites to increase consumer convenience in online shopping. The recommendation systems method applied is Demographic Filtering and Content-based Filtering. Demographic Filtering uses IMDB Weighted Rating calculations which generate recommendations globally and give recommendations based on each product's IMDB Weighted score. Content-based Filtering uses Cosine Distance calculations which generate personal recommendations and give recommendations based on the score cosine distance of each product in the form of a presentation of the similarity of products that have been purchased with other products. This research uses 107 UMKM fashion and craft product data that was obtained from Bogor City Regional Craft Center which sells various kinds of UMKM products from Bogor City and Regency. Data preprocessing is then carried out on the raw data, with the Data Cleaning, Data Transforming and Data Splitting stages which divide the data in a ratio of 80:20. The accuracy of Demographic Filtering Recommendation System reaches 82.7% and Content-based Filtering Recommendation System reaches 100%.
Accurate and Objective Lecturer Appraisal System: Implementation of the LOPCOW Method Sumanto, Sumanto; Radiyah, Ummu; Supriyatna, Adi; Pujiastuti, Lise; Yani, Ahmad; Marita, Lita Sari
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 2 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i2.10188

Abstract

This research proposes the use of the Logarithmic Precursor Chain-Driven Objective Weighting (LOPCOW) method to evaluate the best lecturers in universities.  The LOPCOW method ensures that the assessment covers all aspects of lecturer quality and performance, including education, research, community services, discipline, commitment, cooperation skills, and innovation. The evaluation of lecturers using the scores and ratings provided showed that CDE lecturers were the best, with the highest score of 0.715.  CDE lecturers showed high consistency in all aspects assessed, especially in education, research, and community service.  This was followed by MNO lecturers (0.676), STU lecturers (0.668), XYZ lecturers (0.637), and AFI lecturers (0.627). In conclusion, highly ranked lecturers showed strong dedication to the Tridharma of higher education, with consistent performance and a positive impact on the academic community and the general public.  Future research should focus on developing strategies to improve lecturers' teaching quality by applying new educational technologies and evaluating their impact on student learning. 
SENTIMENT ANALYSIS OF ELECTRIC CAR PRODUCT TRENDS IN INDONESIA USING BM25 AND K-NEAREST NEIGHBOR Alfaro, Ariel -; Mardhiyah, Iffatul -
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 2 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i2.9382

Abstract

The global and Indonesian shift towards electric vehicles (EVs) is driven by efforts to reduce emissions and promote sustainable energy. Social media, especially Twitter, functions as an important measuring tool regarding public sentiment towards electric vehicles in Indonesia, so that it can influence policy making. This research uses the BM25 and K-Nearest Neighbor (KNN) methods to analyze sentiment, which aims to improve EV adoption strategies. Conducted in 2023, this research applies data mining, specifically Knowledge Discovery and Data Mining (KDD), analyzing primary and secondary data descriptively and quantitatively starting with data collection from Twitter, followed by data crawling and initial text processing. Next, labeling, term frequency (TF) and inverse document frequency (IDF) calculations were carried out using the BM25 and KNN methods, with an Evaluation and Validation Diagram that visualized the process. The findings show that negative sentiment dominates at 48% (4800 data), followed by 34% (3400 data) neutral sentiment and 18% (1800 data) positive sentiment. The balanced distribution of sentiment highlights the diverse perceptions of society. BM25 and KNN pre-processing methods effectively reduce overfitting and underfitting, especially in negative and neutral sentiments. Accuracy testing without BM25 resulted in 58.6% to 60.25%, while integrating BM25 with KNN increased accuracy by 12.5% to 71% to 72.75%. Understanding sentiment provides a basis for decision making and policy development, as well as providing insight into public perceptions of electric vehicles in Indonesia. Implications include leveraging positive sentiment for marketing, adjusting strategies, refining pricing, addressing infrastructure and reliability issues, and collaborating with governments to increase adoption of electric vehicles in society.

Page 21 of 22 | Total Record : 217

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