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INDONESIA
KLIK: Kajian Ilmiah Informatika dan Komputer
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : -     EISSN : 27233898     DOI : -
Core Subject : Science,
Computer Science & IT
Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 561 Documents
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Perbandingan Metode Certainty Factor dan Case Based Reasoning Dalam Mendeteksi Penyakit Arteritis Takayasu M Mustaqim; A Gilang Ramadhan; Agus Iskandar
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1735

Abstract

This study aims to compare the effectiveness of two methods, Certainty Factor (CF) and Case-Based Reasoning (CBR), in detecting Takayasu's Arteritis. Takayasu's arteritis is a complex disease with varying symptoms, giving rise to difficulties in correct diagnosis. The main aim of this study was to evaluate their performance in the context of such complex disease detection. The main problem faced is the uncertainty in assessing the level of certainty of diagnosis based on symptoms that vary between patients. The CF method uses the confidence factor principle to measure the level of certainty in a diagnosis, while CBR uses knowledge from previous cases to design a diagnosis for new cases. This study uses a dataset that includes information on symptoms and patient history related to Takayasu's Arteritis. The experimental process involves implementing both methods on the dataset, with results including evaluation of accuracy, sensitivity, and specificity levels. The research results highlight differences in the performance of Certainty Factor and Case-Based Reasoning in detecting Takayasu's Arteritis, providing in-depth insight into the advantages and disadvantages of each method. It is hoped that this understanding can help develop a more efficient and accurate detection system to overcome the complexity of diagnosing Takayasu's Arteritis. The percentage results from the two approaches used in diagnosing Arteritis Takayasu disease have been presented. The Certainty Factor Method indicates a confidence level of 94%, while the Case Based Reasoning Method shows a similarity level of 54%. This significant difference reflects the superiority of the Certainty Factor Method in providing a higher level of confidence in diagnosing this disease compared to the Case Based Reasoning approach.
Implementasi Metode MABAC dengan Pembobotan Entropy Dalam Sistem Pendukung Keputusan Proses Rekruitmen CIO Siti Emalia Saqila; Amelia Hayatul; Agus Iskandar
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1736

Abstract

This research aims to investigate and implement the MABAC (Multi-Attributive Border Approximation) method with weighting based on entropy in a decision support system (DSS) to recommend the most suitable Chief Information Officer (CIO) candidate. As an integral part of upper-level management, the selection of a CIO is a strategic decision that has a major impact on organizational performance. Therefore, the use of SPK in the CIO recruitment process can help organizations make more effective and rational decisions. The MABAC method is used because it is able to overcome situations of uncertainty and ambiguity in CIO recruitment decision making, which involves several interrelated attributes and has the potential to influence CIO performance. Entropy-based weighting is used to measure the level of uncertainty or randomness in attribute data, thereby providing accurate weights for each attribute in the decision-making process. This research uses empirical data from a number of CIO candidates who have various backgrounds and experiences. The data includes a variety of attributes such as education, work experience, technical expertise, and leadership skills. The MABAC method with entropy weighting was used to process this data, and the results were used to produce a ranking of the most suitable CIO candidates. The results of this research indicate that the use of the MABAC method with entropy weighting in the SPK can produce more accurate and reliable CIO recommendations. These results can assist recruiting teams and senior management in selecting the CIO who best fits the organization's needs. Apart from that, this research also identifies the attributes that are most influential in CIO recruitment decisions, so that it can help organizations in designing appropriate skills development or training programs for selected CIO candidates. Thus, this research makes an important contribution to the field of HR management and strategic decision making, especially in the context of CIO recruitment. The results of calculations in this research show that the alternative in the name of Eric Yahya is in first place with a value of 0.405. The results can be a valuable guide for organizations looking to improve their executive selection and recruitment processes
Analisis Sentimen Ulasan Aplikasi Samsat Digital Nasional Pada Google Playstore Menggunakan Algoritma Naïve Bayes Deni Wijaya; Rizki Adi Saputra; Faldy Irwiensyah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1738

Abstract

Digital transformation has become a major factor of change in various aspects of modern life, including business, education, and government. In the current era of digital transformation, the government is trying to improve efficiency and services to the community through the implementation of various technological innovations. The application of digital technology in public services is increasingly widespread, including in the administrative service sector such as the National Digital Samsat (SIGNAL) which allows people to make online vehicle tax payments through the SIGNAL application. User evaluations of this application can provide important insights for service providers. This research aims to analyze the sentiment of user reviews of the National Digital Samsat application on the Google Playstore platform using the Naïve Bayes algorithm. This method is used to classify user reviews into positive and negative sentiment categories. From 2000 reviews taken, 1,665 reviews were categorized as positive and 335 reviews as negative after manual labeling. Data preprocessing using RapidMiner includes cleaning, transform cases, tokenizing, stopword filter, token by length filter, and stemming. TF-IDF weighting is used to give weight to each word in the document. Evaluation of the Naïve Bayes model resulted in an accuracy of 63.61%, with 307 True Positives, 74 True Negatives, 26 False Positives, and 192 False Negatives. Precision was 92.19% and recall was 61.52%. The overall analysis shows that user reviews tend to be more positive towards the SIGNAL app, although there are some negative reviews. This conclusion gives an idea of users' positive perception of the app
SATPOL PP Performance Assessment Using the WASPAS Method in Decision Making Effectiveness Lutfiah, Siti; Priyatna, Bayu; Hananto, April Lia; Novalia, Elfina
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1739

Abstract

Civil Service Police Unit (SATPOLPP) as one of the government's instruments enforces regional regulations and maintains security and protects the community. Leaders have difficulties when evaluating the performance of their members. Manual performance measurement is very ineffective if carried out randomly or by self-assessment. Performance assessments in local government must follow the procedures or rules applicable in local government regulations. Apart from that, the standard for evaluating honorary staff must be based on assessment criteria. In carrying out the analysis, an effective system is needed that can assess the results of member performance. So a performance assessment decision support system is needed using the Weighted Aggregated Sum Product Assessment (WASPAS) algorithm. The WASPAS method has the ability to solve multi-criteria decision problems which are able to reduce errors and optimize in providing assessments and determining alternative highest and lowest values, speed in data management and provide information output results in the form of reports containing performance assessment ranking results. The weights for each criterion are Absence (20%), Work (40%), Collaboration (10%), Discipline (10%), and Knowledge (20%). The results of manual calculations and the application of the WASPAS method show that the highest alternative value obtained a value of 50.5 to the lowest alternative which obtained a value of 26.5 with the same accuracy. so that the evaluation and sanctions obtained can decide who gets ownership and which members can be recommended to extend the work contract using the criteria for consideration. With this calculation system, it becomes faster and more effective in obtaining performance scores for SATPOLPP members and speeding up the leadership decision-making process.
Penerapan Metode Piotroski F-Score Untuk Sistem Rekomendasi Saham Berbasiskan Website Setiyawanto, Setiyawanto; Rastri Prathivi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1759

Abstract

Stock investment is an important aspect in allocating resources in the hope of gaining profits in the future. The Indonesian capital market shows positive growth at the end of 2022, encouraging investors' interest in investing. The problem currently faced by investors is that many investors still have difficulty analyzing the fundamentals of a company because it takes a long time and the complexity of the implementation techniques means that many experience losses when investing. This research aims to develop a stock investment recommendation system by integrating the Piotroski F-Score Method. This method uses nine criteria to assess company performance and provides a score between 0 and 9. Previous research results show that the Piotroski F-Score is effective on stocks with low Book-to-Market (BM) providing a more accurate picture of financial fundamentals. System development uses an Agile Development approach to develop systems because it allows system development faster than other methods and the tools used in system design are UML (Unified Modeling Language). The results of the research are a website-based stock recommendation system to simplify and speed up the time for making investment decisions, and the results of ADRO, ANTM, ESSA, INCO, and ITMG shares achieved the highest score each with a value of 9. The share value that reached the highest value shows that These shares are worth investing in.
Analisis Sentimen Aplikasi Spotify Pada Ulasan Pengguna di Google Play Store Menggunakan Metode Support Vector Machine Wulandari, Cindi; Sunardi, Lukman; Hasbiana, Hasbiana
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1762

Abstract

The Spotify app makes it easy for users to listen to their favorite songs. Usually the Spotify App is accessed on a smartphone so that it can be played at any time.  Today's digital generation can use technology in the form of music, music can affect human feelings and thoughts. The increasing number of Spotify application users on the Google Play Store, raises a variety of user reviews of the application. These reviews can be in the form of positive or negative comments. Addressing this, it is necessary to conduct sentiment analysis in order to provide a deeper understanding of user perceptions and grouping of user reviews of the Spotify application. Sentiment analysis is a case study of opinions, feelings, and emotions expressed in texs. The number of diverse reviews requires classification of reviews into positive and negative classes using the Support Vector Machine method. The purpose of this research is so that it can be examined to what extent the positive and negative reviews can be used as a reference in building the Spotify application to be even better. Object classification is done based on training data that uses the closest distance or similarity to the object for convenience. Using 5000 relevant review data from December 2023 to January 2024. After the labelling stage is carried out into positive and negative classes, there are 3193 positive and 1347 negative comments. The results of sentiment analysis testing using the Support Vector Machine method resulted in an accuracy of 85%, precision 86%, recall 92% and f1-score 89%.
Perancangan Perangkat Lunak Sistem Parkir Kendaraan Menggunakan Metode Design Thinking Maulana, Arlan; Ahmad Syazili; Muhamad Ariandi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1764

Abstract

The first parking system implemented in Indonesia was a manual or traditional parking system. Just like the parking system in Air Kumbang District which still uses a manual parking system. This causes crimes such as motorbike theft and the absence of complete, informative and interactive parking information which is one of the problems with manual parking. Designing a more modern parking system using the Design Thinking method can be used to overcome this problem. Design Thinking is a human-centered approach method. Implementing this design thinking method involves 5 process stages consisting of data collection (emphatize), definition and analysis of the problem (define), solution stage (ideate), creating an interface design (prototype) and testing (testing). This method can be applied to various types of organizations to improve the process of creativity, problem solving, leadership and innovation. Based on research data, it shows that the level of user satisfaction for designing vehicle parking system software with an overall Single Ease Question score from the first user is 6.5 , the second user is 6.6 and the third user is 6.5, with the average of the three Single Ease Question scores being 6.5 on a scale of 1-7. This states that it is accepted by users and easy to understand. By implementing a parking system using the Design Thinking method, it is hoped that it can create a sense of comfort and ensure optimal user safety, so that users can carry out activities efficiently.
Analisis Sentimen Pengguna Terhadap Aplikasi Bing Chat di Google Play Store dengan Metode Naïve Bayes Dimas Cahyo Ramadhan; Faldy Irwiensyah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1769

Abstract

The development of technology that occurs at this time is increasingly rapid, so it can be said to be an era of technological revolution where at this time almost all activities in society have used technology. One of the technologies that emerged in the current era of technological development is artificial intelligence(AI) technology. Artificial intelligence refers to the ability of computers to learn, adapt, and make decisions based on data. Currently, there are many artificial intelligence technologies in the form of applications that can be easily downloaded for free on the Google Play Store, one of which is the application resulting from the partnership between Microsoft and OpenAI, namely Bing Chat. The presence of Bing Chat as one of the artificial intelligence applications on the Google Play Store raises various user reviews while using the artificial intelligence technology. Based on this, a method is needed to analyze the various reviews on the Bing Chat application. This research aims to analyze user sentiment reviews of the Bing Chat application on the Google Play Store with the Naïve Bayes method. A total of 2000 user sentiment review data for the Bing Chat application on the Google Play Store in the January to February 2024 timeframe were collected using the web scrapping method. After going through the analysis process, 1877 sentiment data were obtained with 1653 positive sentiment data and 224 negative sentiment data. The evaluation results of this research on the sentiment of the Bing Chat application on the Google Play Store with the Naïve Bayes algorithm method get the results of the accuracy value of 67.16%, precision 93.53%, and recall 67.39%.
Klasifikasi Kecanduan Bermain Game online Pada Remaja Menggunakan Metode Naïve Bayes Classifier Berbasis Website Suci Pania, Tika; Hidayati, Rahmi; Kasliono, Kasliono
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1782

Abstract

The use of electronic devices such as cellphones, laptops, and others is often found for various reasons, including playing online games. Online games are very popular because they can relieve stress and can be played by various ages, one of which is teenagers aged 10-19 years. However, online games can be detrimental to teenagers. If a teenager plays online games for a long time, that teenager will become dependent on online games. This research creates a system that can help teenagers find out their level of addiction to online games, so that teenagers can overcome their addiction problems. This system classifies addiction to playing online games in teenagers with mild, moderate and severe levels using the Naïve Bayes Classifier method. This system can help teenagers control themselves when playing online games. In determining the level of online game addiction, 5 attributes are used, namely age, gender, place of play, type of game, and length of play. Testing with 150 data and tested with nine comparisons of training data and test data, namely 10:90, 20:80, 30:70, 40:60, 50:50, 60:40, 70:30, 80:20, and 90: 10. Testing is carried out using a confusion matrix to produce accuracy, precision, recall and error rate values. The highest accuracy value is found in comparing training data and test data of 40:60. Accuracy results were 93%, precision was 90%, recall was 89%, and error rate was 6.67%.
Implementasi Question Answering Berbasis Chatbot Telegram Pada Tafsir Al-Jalalain Menggunakan Langchain dan LLM Febrian Rizki Adi Sutiyo; Harahap, Nazruddin Safaat; Surya Agustian; Reski Mai Candra
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1784

Abstract

Technological developments are very important for efficient, accurate and fast information retrieval. Tafsir Al-Jalalain is one of the famous Tafsir Al-Qur’an, and is used as a source of life guidance for muslims. To get information about tafsir, you can go through information media such as the internet or from experts in Tafsir Al-Qur’an. However, to get information it takes a lot of time to filter the information efficiently, accurately and quickly. This problem requires a system that is able to answer human questions accurately, effectively and quickly. In this research, it is hoped that the implementation of telegram Chatbot-based Question Answering using Langchain and LLM will be a solution for providing information on Tafsir Al-Jalalain that is accurate, effective and fast. The Question Answering system will carry out learning on the Tafsir Al-Jalalain data using a language model, namely the Large Language Model, so that it is expected to be able to provide accurate, effective and fast information. The evaluation results of the research by distributing questionnaires to students majoring in Al-Qur'an and Tafsir Science at UIN SUSKA Riau, as many as seven respondents, obtained a percentage of 84.29%

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