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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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KAMUS DIGITAL TANAMAN OBAT MENGGUNAKAN ALGORITMA ROCCHIO BERBASIS MOBILE Arie Qurania
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 17, No 2 (2020): 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.v1i1.2063

Abstract

Medicinal plants are known since the days of ancestors used as natural ingredients for various diseases such as diarrhea, colds, hypertension, diabetes, malaria, dengue fever, stomach ache, intestinal inflammation, cholesterol, and toothache. Medicinal plants can not replace the existence of medical drugs that have been clinically tested but the efficacy of medicinal plants can be used as an alternative treatment. Medicinal plants can be used in several parts of the plant, including leaves, stems, tubers, fruit, roots, and bark. Society generally knows the efficacy and how to mix medicinal plants from the experience of previous parents or through books and writings. Search through books or writings requires a short time compared to searches through digital media such as mobile phones. The research aims to create a digital dictionary of mobile-based medicinal plants which has a search facility based on the words entered, for example, the contents of the medicinal plants. Digital dictionary application of medicinal plants using the pecarian technique with Rocchio algorithm with a total data of 200 medicinal plants.KAMUS DIGITAL TANAMAN OBAT MENGGUNAKAN ALGORITMA ROCCHIO BERBASIS MOBILE
Clean Water Demand Prediction Model Using The Long Short Term Memory (LSTM) Method Sari, Delviani Permata; Karlitasari, Lita; Wihartiko, Fajar Delli
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 2 (2023): 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.v20i2.8060

Abstract

Cities or districts as population centers with various service facilities, really need the provision of clean water. The agency that handles clean water in Indonesia is the Regional Drinking Water Company (PDAM). PDAMs were established in every city and district in Indonesia as agencies that serve the community's need for clean water. One of them is the Regional Public Company (Perumda) Tirta Pakuan and as time goes by the number of customers will definitely increase so that the need for clean water will also increase. The purpose of this research is to create a Clean Water Demand Prediction Model using the Long Short Term Memory (LSTM) Method to find the most optimal modeling. The data in this study were obtained from data reports is from Perumda Tirta Pakuan. The prediction model development process is carried out through Visual Studio Code tools. To find a model with the smallest error rate using various ratios, namely 80:20, 70:30, 60:40, and 50:50, then testing is also carried out based on the number of different hyperparameter values in batch sizes 5, 10, 15, 20, 25 and max epoch 50, 100, 150, 200, 250. From all the experiments that have been carried out, the most optimal is batch size 5 and epoch 50 with a ratio of 60:40 for water production to get RMSE 0.4862 and MAPE 2.5252% while for the amount of water use with a ratio of 50:50 get RMSE 0.4674 and MAPE of 2.5163%.
Implementation of EDAS Method in the Selection of the Best Students with ROC Weighting Darwis, Dedi; Sulistiani, Heni; Megawaty, Dyah Ayu; Setiawansyah, Setiawansyah; Agustina, Intan
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 2 (2023): 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.v20i2.7904

Abstract

This study aims to provide recommendations for the best students to be selected using the EDAS method and ROC weighting, so as to help schools in decision making. The EDAS method requires a lot of input, and preference must be precise in the determination of the weight of the criteria. To fix the problem of weighting criteria in the EDAS method, the Centroid Rank Order (ROC) method is used. ROC is a simple method used to assign weight values to each criterion used. The results of this study provide recommendations for the best students to be selected using the EDAS method and ROC weighting, so as to help schools in decision making. The application of the EDAS method in the selection of exemplary student candidates resulted in exemplary prospective students obtained on behalf of Hadi Santoso with a final score of 0.70885 and obtained 1st rank. The results of these recommendations can help the school determine the selection of the best students by applying the EDAS method and ROC weighting.
C4.5 Algorithm Implementation to Predict Student Satisfaction Level of Lecturer’s Performance in the Covid-19 Pandemic Ledoh, Juan Rizky Mannuel; Andreas, Ferdinandus Elfanto; Pandie, Emerensye Sofia Yublina; Amos Pah, Clarissa Elfira
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 2 (2023): 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.v20i2.8284

Abstract

Implementation of education during the emergency period of Covid-19 in Higher Education was carried out at home through online/distance learning. The lecturer is one of the key holders of success in the learning process. Lecturer performance is a main factor needed to improve education and service quality in online learning. In this study, the authors implemented the C4.5 algorithm using RapidMiner 9.10 app to predict student satisfaction with lecturer performance during the Covid-19 pandemic. The data in this study were obtained from a questionnaire distributed to active students in the Computer Science Study Program (class of 2016 - 2021) at the University of Nusa Cendana with 942 records. The attributes used in this study were the lecturer's age, gender, suitability of learning media (SLM), and the competencies of Pedagogic Competence (PeC), Professional Competence (PrC), Personal Competence (PsC), and social competence (SC), with the level of student satisfaction as the target class divided into two, namely Satisfied and Dissatisfied. The dataset is processed using RapidMiner and produces 11 decision rules which show that the attribute PeC has the most significant influence on the level of student satisfaction with lecturer performance during the Covid-19 pandemic and the test results of the decision tree model using cross-validation. The test results show that the C4.5 algorithm has a good performance in predicting levels of student satisfaction with an accuracy rate of 94.8%, precision for the prediction class Dissatisfied and Satisfied of 92.23 % and 95.52%, and recall of the actual Dissatisfied and Satisfied classes of 85.2% and 97.77%.
Identification of Significant Proteins in Hypertension Using The Clustering Molecular Complex Detection (MCODE) Method Setiani, Lusi Agus; Kusuma, Wisnu Ananta; Zulkarnaen, Silvia Alviani
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 2 (2023): 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.v20i2.7905

Abstract

Hypertension is a condition where the systolic blood pressure value is more than 140 mmHg and the diastolic blood pressure value is more than 90 mmHg. A significant protein is a protein that has the greatest effect or is the center of protein regulation in all biochemical processes. The purpose of this study was to determine the significant protein that has the greatest ef- fect on hypertension by using the clustering Molecular Complex Detection (MCODE) method which will identify areas in the network with the highest density value locally and to determine the mechanism of action of the significant proteins obtained in the setting blood pressure using Gene Ontology and Kyoto Encyclopedia and Genome Analysis (KEGG) by looking at protein signaling pathways for hypertension. The results showed that the STAT3, MAPK3, AKT1, and EDN1 proteins were significant proteins involved in the mechanism of the response to leptin, the ERK1 and ERK2 cascades, the process of nitric oxide biosynthesis, and the cellular response to ROS.
Web Design for Stroke Early Detection Using Decision Tree C5.0 Purwanti, Endah; Nor, Reza Ummam Nor Ummam; Soelistyono, Soegianto
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 2 (2023): 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.v20i2.8265

Abstract

Stroke is a disease that needs serious attention because it can cause disability and even death. According to World Health Organization (WHO) in 2022, stroke is the second leading cause of death and a leading cause of disability in the world. In Indonesia, stroke is the first leading of non-communicable disease proportion according to Riset Kesehatan Dasar in 2018. This study aims to design a web application that can help stroke early detection in a person so that people are more concerned about preventing a stroke. This study used Decision Tree (DT) C5.0 method by utilizing 10 stroke risk factors to analyze the risk of stroke in a person. Decision Tree method can break down complex datasets into several simple rules illustrated by a tree, hence the name Decision Tree. The DT C5.0 is one kind of Decision Tree method that has fast performance in classifying data compared to other methods. Therefore, this study observes how DT C5.0 works in detecting stroke risk. The output of this web application is a statement whether a person has a stroke risk or not. The secondary dataset used for model development totaled 5,109 data consisting of 249 stroke patient data and 4,860 non-stroke patient data. In this study, data balancing and cross validation were carried out so that the performance of the training results model was obtained, namely accuracy 83.54%, precision 78.67%, sensitivity 92.20%, and specificity 74.87%. Furthermore, the performance of the test results model is accuracy 84.42%, precision 79.26%, sensitivity 93.10%, and specificity 75.80%.
Topic Modeling LDA and SVM in Sentiment Analysis of Hotel Reviews Erniyati, Erniyati; Harsani, Prihastuti; Mulyati, Mulyati; Fahriza, Lutfi Dani
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 2 (2023): 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.v20i2.7604

Abstract

The number of visitor comment review data that enters the TripAdvisor and Agoda sites continues to grow over time, this makes it difficult for the hotel to obtain overall information from all comment reviews. Therefore, the purpose of this study is to apply topic modeling and classifying in the analysis of hotel service sentiment.  The data for comment reviews were obtained from 3 five-star hotels, namely 1-HTL, 2-HTL and 3-HTL. The hotel has a five-star rating and has the most comments compared to other hotels in Jakarta. The topic modeling method using Latent Dirichlet Allocation (LDA) in this study succeeded in dividing the comments into several topics that were often discussed from Indonesian and English comments regarding the hotel services provided. By using Support Vector Machine (SVM) obtained the number of positive, negative and neutral comments.
Analysis of Tomato Ripeness by Color and Texture Using Cielab and K-Means Clustering Denih, Asep; Negara, Teguh Puja; Marzuki, Ismail
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 2 (2023): 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.v20i2.8311

Abstract

Humans have limitations, including in the identification of tomatoes. With the nature of limitations, it makes it difficult for humans to identify the ripeness of tomatoes in large quan- tities. So far, the selection and determination of the quality activity of tomatoes is carried out manually, resulting in a less uniform product. Manual identification of tomato ripeness has many disadvantages caused by many factors, such as fatigue, lack of motivation, experience, proficiency and so on. This study aims to create a tomato maturity level analysis system based on color and texture using CIELAB and K-Means clustering as a method to determine tomato maturity precisely and accurately. This system displays five images, namely RGB, CIELAB, K-Means clustering, binary and grayscale images, after entering the tomato image, the image will be processed using the five images and the results of extracting characteristics from the tomato will come out. The accuracy rate of tomato ripeness has an average value of 92.70%. The benefit of this research is that it can save time in classifying tomato ripeness and make it easier to determine tomato ripeness based on color.   
Chatbot PTIPD Customer Care Center Service using Dialogfow Ndruru, Arlan Joliansa; Fikry, Muhammad; -, Yusra
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 2 (2023): 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.v20i2.8281

Abstract

Chatbot research is a unique innovation in the development of Artificial Intelli- gence and has promising prospects in the field of Education. One form of information service available at the university is the Customer Care Center (C3) PTIPD UIN Suska Riau, which is responsible for handling problems submitted by students. However, with so many questions or problems submitted to the PTIPD Customer Care Center, it is difficult for the PTIPD Cus- tomer Care Center to respond to student questions submitted, the service becomes ineffective and the response to the answers to the problems submitted becomes late. To overcome this problem, chatbot development was carried out for PTIPD UIN Suska Riau Customer Care Center Services using Dialogflow to improve services and overcome existing problems. Di- alogflow as conversation development platform that uses natural language processing (NLP) to understand and interpret user intent in conversations. Through User Acceptance Test (UAT) testing, the chatbot managed to achieve an acceptance rate of 84% overall. This shows that users, in this case, students respond positively to the use of chatbots in PTIPD Customer Care Center services. In addition, Usability Testing was also conducted to evaluate the level of usability of the chatbot. Based on this test, the chatbot achieved a score of 76, which indicates a good level of usability in interaction with users. The test results illustrate that the chatbot at the Customer Care Center PTIPD UIN Suska Riau has provided a positive user experience.
The Sustainability of The Netflix's Business Processes With Knowledge Risk Management Approach Gustian Rama Putra; Wahyu Sardjono; Taruma Leo Wijaya; Erna Selviyanti; Asep Saepulrohman
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 1 (2023): 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.v1i1.5935

Abstract

The Squid Game film is a film that is very familiar among the public because the story, storyline, and also the quality provided gives a very good impression that can be accessed by the public through an application called Netflix. Not surprisingly, many people remember their childhood in these stories. However, on the side of its very global fame, there are several issues that must be faced by Netflix in showing the Squid Game film that can threaten the sustainability of Netflix's business process itself. Through this research, we will analyze the factors that cause Netflix to face the worst scenario in showing the Squid Game film through Factor and Regression analysis on a predetermined model. The results stated that there were 9 factors that caused Netflix to have to deal with this problem.

Page 19 of 22 | Total Record : 217

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