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Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Published by Ikatan Ahli Informatika Indonesia
ISSN : -     EISSN : 25973584     DOI : -
Core Subject : Science,
Computer Science & IT
Seminar Nasional Sistem Informasi dan Teknologi (SISFOTEK) merupakan ajang pertemuan ilmiah, sarana diskusi dan publikasi hasil penelitian maupun penerapan teknologi terkini dari para praktisi, peneliti, akademisi dan umum di bidang sistem informasi dan teknologi dalam artian luas.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 472 Documents
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Clustering Data Penerimaan Mahasiswa Baru Universitas Handayani Makassar Menggunakan Algoritma K-Means A. Ade Rosali Saputra; Samsart Deandi Palumery; Hazriani, Hazriani; Yuyun, Yuyun
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This research aims to group new student data for the 2022 academic year at Handayani University Makassar by utilizing a data mining process using the K-Means Clustering algorithm. Implementation using Rapidminer software is used to help determine accurate values. The data used in carrying out this research consists of 4 (four) attributes, namely, school origin, average UAS (final semester exam) score, gender and chosen study program. The research process begins by selecting data and then transforming the data into a numerical group. It is hoped that the results of this research can help universities in improving appropriate promotional strategies in each study program at Handayani University, Makassar.
Penentuan Prioritas Penyewaan Armada Angkutan Crude Palm Oil (CPO) Berbasis Multi Platform Menggunakan Algoritma Electre Ardyansah, Ardyansah; Herlinah, Herlinah; Billy Eden William Asrul
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

CV. ABN Transport is a company providing Crude Palm Oil (CPO) transportation fleets in East Kalimantan with a fleet of seven (7) units, capable of transporting 8 tons of CPO liquid. This research aims to design a multi-platform based application that utilizes the ELECTRE algorithm to determine fleet rental priorities and the Google Maps API to obtain rental origin and destination locations and calculate mileage. The main objective is to overcome delivery schedule problems that often occur and ensure the effectiveness and efficiency of utilizing the rented fleet. This research uses the ELECTRE method to rank tank truck fleet rental priorities by collecting data through observation, interviews and literature study. The research design uses Unified Modeling Language (UML) functional modeling, including use case diagrams, activity diagrams, sequence diagrams, and class diagrams. In the application, the RESTful API architecture is used in the Laravel framework to enable easy and effective API creation and management. On the mobile side, a Model View Controller (MVC) is used in the Flutter framework and the database is implemented with MySQL. Based on the results of application testing, 8 respondents as a sample, 50% of users agreed that the application provides rental order recommendations based on the weight of the assessment criteria and helps in making prioritization decisions. Apart from that, 33.33% of respondents strongly agreed and 16.67% somewhat agreed with the statement. In conclusion, the application designed can assist CV ABN transport to determine CPO fleet rental prioritas more affectively and efficiently due to the use of the ELECTRE Algoritm and Google Maps API integraton to manage the rental of pick up location and destination.
Pengalaman Pengguna Aplikasi Konferensi Video Google Meet Selama Pandemi Covid-19: Analisis UEQ Garin Caesar Syanugiri; Purnawarman Musa; Witari Aryunani
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The Covid-19 pandemic has changed how we work, learn, and communicate. One of the key tools in responding to these challenges is video conferencing applications, such as Google Meet. This article explores user experiences with Google Meet during the Covid-19 pandemic using the User Experience Questionnaire (UEQ) method to analyze various aspects of user experience. The research steps in applying the UEQ data analysis method encompass data collection (scores) through the UEQ questionnaire, conducting descriptive analysis to identify trends and patterns in user experiences, and interpreting the results to comprehend their implications. The analysis outcomes aid researchers in summarizing user experiences, pinpointing areas for improvement, and offering pertinent recommendations. The Attractiveness variable receives a high score, reflecting a user-friendly and efficient interface. Perspicuity also obtains positive scores, indicating the ease of using the application. Efficiency and Dependability likewise achieve high scores, signifying that users perceive the application as reliable for important meetings. Stimulation and Novelty show a substantial improvement, indicating that users feel engaged and interested while using Google Meet. The UEQ analysis results provide valuable insights into how Google Meet has become an effective and satisfying solution to address the challenges individuals and organizations face during the Covid-19 pandemic. This article also discusses the implications of these findings in the context of education, work, and remote communication.
Implementasi Algoritma Hue Saturation Value (HSV) Pada Penentuan Kualitas Beras Berbasis Android Yuliana Aprilia Anwar; Billy Eden William Asrul; Sitti Zuhriyah
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Rice is the staple food of most Indonesian people. Rice contributes more than 22% of global energy intake. Indonesia, especially South Sulawesi is a major rice producer where rice production is around 92% of total world production 1.2 For nations in Asia. This research aims to implement the Hue Saturation Value (HSV) algorithm for extracting color features in Android-based rice quality determination. In this research there were 600 images of rice which were divided into 80% training data and 20% testing data. Training samples are used for color feature extraction by applying Red, Green and Blue (RGB) features to the Hue Saturation Value (HSV) method. Next, image identification is carried out to determine the quality of the rice using color features. From the test results using the confusion matrix at a value of K=7, precision was 82%, recall was 90%, F-1 score was 86%, and accuracy was 85%
Implementasi Algoritma Genetika Untuk Penjadwalan Ujian Pada Universitas Handayani Makassar Syahrul Saleh; Najirah Umar; M Adnan Nur
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This research relates to Handayani University Makassar formerly STMIK Handayani Makassar. The university offers various programs in the field of computer science and IT management. One of the main problems faced is that the preparation of the exam schedule is currently still done manually using Microsoft Excel, prone to errors and time consuming. This research aims to develop an automated system for preparing exam schedules using genetic algorithms. Genetic algorithm is a computer method that helps find optimal solutions in exam scheduling. The results of this study are expected to help improve the efficiency and accuracy of the preparation of the exam schedule at Handayani University Makassar.
Optimasi Penjadwalan Perkuliahan Pada Universitas Handayani Makassar Menggunakan Algoritma Particle Swarm Optimization Dwiky Darmawansyah; Najirah Umar; Nurfaedah, Nurfaedah
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Lesson planning is a regular task that every educational institution does right from the beginning of the semester. However, creating an optimal study schedule is quite difficult because there are many interrelated variables that require careful management. Current class scheduling at Handayani University Makassar is still done manually, including searching for empty columns and placing class schedules in those columns. The director of BAAK UHM, who was responsible for preparing the conference program, felt overwhelmed. Because the planning done so takes a lot of time. Therefore, the established course program must be regularly revised. One method considered to provide a solution to scheduling problems is swarm optimization (PSO). PSO if translated means Particle Swarm Optimization. This algorithm can solve the problem by randomly forming particles in the initial population, evaluating appropriate values, and updating particle velocities and positions. This is intended to address the issue of proper function of each particle. The data includes 225 course data plus 1 speaker's request schedule data. This study was successful in achieving a course schedule consistent with the course conditions and policies at UHM. From the test results, the lesson planning application using the PSO algorithm can provide an optimal class schedule that is consistent with the teacher's teaching schedule preferences. The calculation time used is less than 5 seconds.
Implementasi Algoritma Support Vector Learning Terhadap Analisis Sentimen Penggunaan Aplikasi Tiktok Shop Seller Center Sarina, Sarina; Adam M Tanniewa
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

E-commerce is experiencing rapid growth in Indonesia, followed by the increasing popularity of the Tiktok Shop application. This research aims to conduct sentiment analysis on user reviews of the Tiktok Shop Seller Center application on the Google Play Store using the Support Vector Learning (SVM) method and Text Mining techniques. This research collects review data in Indonesian from May to July 2023. This data includes ratings, comment content and review dates. The sentiment analysis results allow grouping reviews into positive or negative, and SVM with various kernels (Linear, RBF, Polynomial, and Sigmoid) is used to classify the sentiment. This research has the potential to provide important insights into users' views of the Tiktok Shop Seller Center and contribute to the development of sentiment analysis in the context of e-commerce in Indonesia.
Analisis Data Kementrian Agama Kota Bitung Menggunakan Metode Data Science Olivia Manabung; V P Rantung
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The Ministry of Religion (Kemenag) of the Republic of Indonesia was born on January 3 1946, the Ministry of Religion is the ministry tasked with assisting the government in the field of religion. In this research, researchers will predict the number of people for each religion in the next two years using data on the number of people from several years ago using the ARIMA Time Series forecasting method using the EView 12 application and researchers will also group places of worship based on sub-district religions using Clutering. K-Means uses the RapidMiner application to see the number of places of worship for each religion in each sub-district. The aim of this research is to help the Ministry of Religion of Bitung City in creating a dashboard to display data information and data reports about the number of people in each religion, predicting the number of people in the next two years, grouping places of worship for each religion, and the number of places of worship for each religion in the city of Bitung. The results obtained from the prediction of the number of Catholics in the city of Bitung in 2023 will be 5,563 people and in 2024 there will be 5,301 people, the predicted number of people from the Islamic religion in 2023 will be 32,768 people and in 2024 there will be 29,988 people, and the results are obtained from the prediction of the number of people from the christianity in the city of Bitung in 2023 there will be 113,242 people and in 2024 there will be 134,433 people. This produces 3 cluster models containing cluster 0 4 items, cluster 1 3 items, and cluster 2 1 items. The cluster starts from the number 0 because when discussing programming the number 0 is the first number in the numbering sequence
Analisis Sentimen Ulasan Produk di E-Commerce Bukalapak Menggunakan Natural Language Processing Elsa Sera; Hazriani, Hazriani; Mirfan, Mirfan; Yuyun, Yuyun
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This research discusses the analysis of sentiment in product reviews on E-Commerce Bukalapak using Natural Language Processing (NLP). The study aims to fill the knowledge gap regarding the analysis of product reviews in online stores in Indonesia, specifically Bukalapak. The data used in this research were collected from various product categories, such as clothing, electronics, cosmetics, and others. The method employed in this study was the TF-IDF method to train the Naive Bayes model. The results of the research show that the Naive Bayes model trained using the TF-IDF method achieved an accuracy of 88%. This indicates that the model has good capability in predicting the sentiment of product reviews. The analysis of positive reviews reveals customer satisfaction with product quality, fast delivery, reasonable pricing, and receiving items as expected. On the other hand, the analysis of negative reviews uncovers the mismatch between customer expectations and the actual conditions regarding color, delivery, and product orders. This study contributes to a deeper understanding of sentiment analysis in product reviews on E-Commerce Bukalapak. The insights from this analysis can be utilized by Bukalapak to enhance the quality of their products and services, providing a more satisfying experience for customers.
Penerapan Metode K-Means Clustering Dalam Mengelompokkan Data Penjualan Obat pada Apotek M23 Nurul Azmi; Hafsah HS; Yuyun, Yuyun; Hazriani, Hazriani
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Planning for the need for the right medicines can make the procurement of medicines efficient and effective so that the medicines can be sufficiently available as needed and can be obtained when needed. At the current M23 Pharmacy, sometimes there is a shortage or overstock of medicines. To overcome these problems a data mining method is applied by analyzing drug use to produce information that can be used as drug inventory control and planning. The method used in this study is the K-Means method. The K-Means clustering method aims to group data that has the same characteristics into the same cluster and data that has different characteristics are grouped into other clusters. As for determining the best number of clusters using the Davies Bouldin Index (DBI) method. The results of this study determined that the best number of clusters was 2 clusters, the drug data grouping consisted of cluster 1 with low drug use consisting of 144 types of drugs and cluster 2 with high drug use consisting of 6 types of drugs.

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