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Jurnal Tekinkom (Teknik Informasi dan Komputer)
Published by Politeknik Bisnis Indonesia
ISSN : 26211556     EISSN : 26213079     DOI : https://doi.org/10.37600/tekinkom
Core Subject : Science,
Computer Science & IT
Jurnal TEKINKOM merupakan jurnal yang dimaksudkan sebagai media terbitan kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai isu Ilmu - ilmu komputer dan sistem informasi, seperti : Pemrograman Jaringan, Jaringan Komputer, Teknik Komputer, Ilmu Komputer/Informatika, Sistem Informasi, dan Multi Disiplin Penunjang Domain Penelitian Komputasi, Sistem dan Teknologi Informasi dan Komunikasi, dan lain-lain yang terkait. Artikel ilmiah dimaksud berupa kajian teori (theoritical review) dan kajian empiris dari ilmu terkait, yang dapat dipertanggungjawabkan serta disebarluaskan secara nasional maupun internasional.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 407 Documents
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RANCANG BANGUN SISTEM INFORMASI DESA BANGKAL KECAMATAN BINANGUN BERBASIS WEB DAN MOBILE Rafi Danish; Selfi Artika
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.720

Abstract

The rapid development of technology has had various negative and positive impact on humans, especially in the fields of communication and information. the positive impacts is the speed of the process delivering information via the internet such as websites, mobile applications and so on. For some people who are far from the city center, the process of obtaining and conveying information is still very limited, due to various internal and external factors. This research uses observation and interviews methods village officials and resident. Therefore, this website was created using PHP, Flutter Bootstrap, and MySQL Database with the aim of helping village communities obtain information services and assingting village goverment in providing information service facilities and being able to explore or introduce village culture and potential through web and mobile-based applications. The outcome of this study is an information system for villages in the form of a website and mobile that can manage village information, namely in the form of information services in the form of activites, news, galleries, correspondence services and this system can be accessed by anyone to get information about Bangkal village.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN PELATIH KEGIATAN EKSTRAKURIKULER MENGGUNAKAN METODE MOOSRA Arya Widana; Volvo Sihombing; Ibnu Rasyid Munthe
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.1018

Abstract

This research aims to solve the problem of selecting extracurricular coaches. To assist the Faculty of Science and Technology at Labuhanbatu University in selecting trainers for extracurricular activities, a Decision Support System was designed using the MOOSRA (Multi-Objective Optimization based on Ratio Analysis) method. The Decision Support System (DSS) using the MOOSRA method was implemented to increase objectivity and efficiency in selecting trainers. Research methods include preliminary studies, determining criteria (experience, achievement, academics, skills, leadership), and data collection. MOOSRA is used to optimize decisions based on criteria. The ranking results show the three best coaches: Coach02, Coach08, and Coach07. The existence of this decision support system can help make it easier for the Faculty of Science and Technology, Labuhanbatu University, to select extracurricular trainers more quickly and efficiently so that they can support and increase effectiveness in supervising student extracurricular activities.
PENERAPAN DATA MINING UNTUK KLASIFIKASI BERITA HOAX MENGGUNAKAN ALGORITMA NAIVE BAYES Saut P Tamba; Agusteti Laia; Yudika Kristian Butar-butar; Anita Anita
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.922

Abstract

The research aims to develop a classification model that is effective in identifying hoax news. The rapid development of information technology has had a significant impact on the dissemination of information, especially in the context of the spread of hoaxes via the internet. Hoaxes, or fake news, can cause misperceptions and negative impacts on various aspects of people's lives. To classify hoax news, this research was carried out using the Naive Bayes algorithm. The data used comes from various sources and involves stages of data collection, data analysis, and preprocessing processes. Modeling uses the Naive Bayes Algorithm, which applies the law of probability, to calculate the confidence or probability that a news item falls into the fraud category. The data preprocessing process includes tokenization, case transformation, stopwords filter, and tokens filter (by Length), which aims to improve the quality of the analyzed data. Model evaluation was carried out using cross-validation, confusion matrix, and classification report methods. The evaluation results show that the model accuracy is 73.91%, with a deviation of 1.04%. The results of this research can be used to classify hoax news properly. This model can be used as an initial reference in developing more complex prediction models.
ANALISIS METODE ALGORITMA K-NEAREST NEIGHBOR (KNN) DAN NAIVE BAYES UNTUK KLASIFIKASI DIABETES MELLITUS Muhardi Saputra; Johannes Putra Sidabuke; Ryan Pangeranta Sinulingga; Reslina Br Tamba
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.942

Abstract

This study aims to compare the performance of two algorithms in detecting diabetes mellitus, which is a metabolic disorder caused by insufficient insulin production by the pancreas. In this research, we used two algorithms, namely Naive Bayes and K-Nearest Neighbor (KNN), to carry out analysis on the diabetes mellitus dataset used. The Naive Bayes algorithm is a statistical algorithm used to classify and predict the probability of certain classes. Meanwhile, the K-Nearest Neighbor (KNN) algorithm is used to classify new objects based on their similarity to nearby objects. This study utilized 9 variables, including number of pregnancies, glucose levels, blood pressure, skin thickness, insulin, Body Mass Index (BMI), family history of diabetes, age, and diagnosis results. The dataset used consists of 2000 data obtained from KAGGLE. The classification process is carried out by importing data into Microsoft Excel, designing the process, and then analyzing the data using Google Colab by applying the K-Nearest Neighbor and Naive Bayes algorithms. The research results show that the K-Nearest Neighbor algorithm provides a higher level of accuracy compared to the Naive Bayes algorithm.
CLUSTERING HASIL PANEN UBI KAYU MENGGUNAKAN ALGORITMA K-MEANS Ratih Yulia Hayuningtyas; Ida Darwati
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 1 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i1.1327

Abstract

Cassava is one commodity that has the potential to grow the country's economy. Cassava is a primary food requirement besides rice and corn. This research discusses the grouping of cassava products in the Trenggalek area, the data collected will later be formed into a group or cluster. There are 3 clusters created, namely high cluster, medium cluster and low cluster, to determine which data will enter the 3 clusters. Clustering is an analysis method of data mining. This research uses an algorithm, namely K-Means, to process cassava production results into a cluster. The results of the research produced a high cluster of 3 items, a medium cluster of 1 item and a low cluster of 10 items. Judging from these results, there are still many areas in Trenggalek that produce low quantities of cassava. This research can provide strategies or information to increase cassava production in the future.
MODEL SMART SELLING TECHNOLOGY DALAM PENGELOLAAN PRODUK HILIR PETANI, NELAYAN DAN INDUSTRI KREATIF MASYARAKAT DESA UNTUK MEWUJUDKAN UMKM/IKM GO GLOBAL PADA DESA SIRUKKUNGON Beatrice Nathania; Andi Setiawan; Erikson Damanik
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.983

Abstract

The urgency of this research is to build a Smart selling Technology model in Sirukkungon Village MSMEs/IKMs. In the current digital era, MSMEs/IKMs should apply technology in processing their downstream products, but in reality Sirukkungon Village MSMEs/IKMs have still not adopted technology. This is due to the lack of human resources who understand technology. Through this Smart selling Technology Model, all MSME/IKM players, including farmers, fishermen and creative industries, get a technology-based downstream product management platform so that the products they produce can be sold across provinces and even the world. In addition, the business world will be equipped with knowledge about business management. intelligent targeting downstream products that have high selling power so that the system to be built can really be utilized optimally. The aim of this research is to produce a smart, online-based and integrated sales system technology plan/model to manage downstream products for farmers, fishermen and creative industries in Sirukkungon Village to realize MSMEs/IKM Go Global. With the model that will be built, agricultural, fishery and community creative industry products can be managed optimally and have high selling quality. The analytical method used is qualitative analysis and design. The results of this research are a system design and strategy for the Smart selling Technology Model in Managing the Downstream Products of Farmers, Information Fishermen and Creative Industries in the form of a sales system designed to be web-based and can be used for the sales process of products produced by the community's creative economy.
ANALISIS KUALITAS JARINGAN INTERNET DAN NIAT BELAJAR MAHASISWA MENGGUNAKAN K-MEANS CLUSTERING Christian Imanuel Lendo; Dian Chandra
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.1012

Abstract

Student internet networks in online-based lectures during the pandemic yesterday were a must-have for students. This research has the aim of providing awareness of Satya Wacana Christian University Faculty of Information Technology data collection about the effect of internet quality on student learning intentions which can be analyzed with technology using K-Means Clustering. K-Means Clustering is used to group data sets into several clusters. Data features in the form of an internet network quality scale based on perceived service quality and student learning intentions obtained from Satya Wacana Christian University students, Faculty of Information Technology, Informatics Engineering study program through distributing questionnaires. The results of the questionnaire in the form of numbers on a Lickert scale are used in RapidMiner for clustering using K-Means Clustering. The results of the analysis on the student data studied have 37 data having many clusters k = 4 obtained using Elbow Method. Of the student correspondents who have good internet quality, namely C2 by 27.02% and C4 by 8.1%, only 3 correspondents or 8.1% of the total data have good learning intentions. While the results of students internet quality are poor, students study intentions are not significant because the difference in data between C1 and C3 is only a little. The results of C1, namely students who have poor internet quality and poor learning intentions, are only 11 correspondents or 29,72% of the total data while C3, which includes students who have poor internet quality but have good learning intentions, is 13 correspondents or 35.13% of total data. Based on this research, K-Means Clustering can be used to group students in viewing and making decisions online learning.
SEGMENTASI PELANGGAN MENGGUNAKAN K-MEANS CLUSTERING DI TOKO RETAIL Achmad, Syifa Latifah; Fauzi, Ahmad; Rahmat, Rahmat; Indra, Jamaludin
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1226

Abstract

Advancements in information technology have transformed various aspects of human life, including the business world. Companies are required to use technology and data effectively to enhance their competitive advantage. One increasingly relevant strategy is Customer Relationship Management (CRM), where customer data is the main focus. Consumer data segmentation is an approach used to group customers based on certain characteristics. In this study, the K-Means Clustering algorithm is applied to consumer data segmentation to improve the marketing strategy of a store. The study begins with the collection of customer data from the Dan+Dan Telukjambe 2 store, followed by Exploratory Data Analysis (EDA) to understand the patterns and characteristics of the data. Preprocessing steps are carried out to ensure the data is ready for use, including removing irrelevant columns, handling missing values, and data transformation. Principal Component Analysis (PCA) is used to reduce data dimensions before applying K-Means Clustering. The Elbow Method and Silhouette Score are used to determine the optimal number of clusters. The study results indicate that the optimal number of clusters is six. Evaluation using the Silhouette Coefficient provides an average coefficient value of 0.66, indicating good clustering quality. Further analysis shows different distributions of age, purchasing power, occupation, and marital status in each cluster, providing deep insights into customer segments. The resulting clusters offer valuable information for developing more effective and targeted marketing strategies
PENGEMBANGAN PERMAINAN ROLE PLAY GAME UNTUK MENDUKUNG PEMBELAJARAN PERAKITAN PERANGKAT KERAS KOMPUTER Hernawan, Dicky; Sukirman, Sukirman
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1801

Abstract

This study aims to develop an innovative educational tool in the form of game with genre RPG (Role Playing Game) to support the learning of computer hardware assembly in vocational high schools (SMK). Utilizing the Multimedia Development Life Cycle (MDLC) method, the development process consists of six stages: conceptualization, design, material collection, component integration, testing, and distribution. The game is designed with map-based missions simulating real-life computer assembly scenarios, using RPG Maker MV as the primary platform. The participants involved in this study consisted of 25 students, including 9 males and 16 females, aged 16–17 years. The instrument used for data collection was the User Experience Questionnaire (UEQ), which employs a questionnaire to evaluate the level of user experience across six variables. Based on the analysis conducted, most variables were categorized as "Excellent," with scores of 2.60 (Attractiveness), 2.50 (Perspicuity), 1.77 (Efficiency), 1.62 (Dependability), 2.50 (Stimulation), and 2.37 (Novelty). Thus, it can be concluded that the developed RPG game can be effectively used to support learning in computer assembly.
PENERAPAN ALGORITMA SUPPORT VECTOR MACHINES DAN RANDOM FOREST DALAM ANALISIS SENTIMEN ULASAN APLIKASI IDENTITAS KEPENDUDUKAN DIGITAL Ramadhan, Rizky Agung; Rohana, Tatang; Mudzakir, Tohirin Al; Wahiddin, Deden
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1595

Abstract

The Digital Population Identity (IKD) application, developed by the Directorate General of Population and Civil Registration, aims to streamline access to digital documents and reduce reliance on printed KTPs. Despite its benefits, user reviews from the Play Store highlight significant issues. This research aims to analyze user sentiment towards the IKD application using Support Vector Machines (SVM) and Random Forest algorithms. The study employed these models to classify sentiment in user reviews and used word cloud analysis to further understand the feedback. Results indicate that both the Random Forest and SVM models struggled with accuracy, achieving only 19.25% and 18% respectively. The word cloud analysis revealed a high prevalence of negative reviews, reflecting the app's low rating. These findings suggest that the current sentiment analysis methods are insufficient for capturing the public's opinion on the IKD application, providing crucial insights for improving future digital population identity management strategies.

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