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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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Perancangan Website Sistem Informasi KONI Menggunakan Metode Design Thinking Ebimbi, Selgi Agilsa; Anwar, Sariyun Naja; R Soelistijadi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1852

Abstract

KONI Kendal Regency is an achievement sports organization in the Kendal Regency area which has various activities and achievements from athletes to be informed to the general public through a platform that can be accessed by many people. However, so far the Kendal Regency KONI administrators still use manual data and information recording so this is less effective and efficient. Therefore, this research provides a solution, namely a website-based information system as a promotional tool for organizations with the community. The process of developing the Kendal Regency KONI information system website uses the Design Thinking method to determine solutions to existing problems. The Design Thinking method is a problem solving method that focuses on the user. There are five stages that must be carried out in the Design Thinking method, namely empathize, define, ideate, prototype and test. This research resulted in a prototype design for the KONI Information System for Kendal Regency. Based on usability testing using the System Usability Scale (SUS) calculation, the final SUS score was 75 with a Grade Scale of "B" and received an Adjective Rating of "Excellent". The results of these calculations show that the prototype design meets the Acceptable requirements in the usability testing assessment. Thus, the implementation of this information system is expected to increase the accessibility and quality of information conveyed to the public regarding the activities and achievements of Kendal Regency KONI athletes
Kombinasi Metode MOORA dan Rank Order Centroid dalam Sistem Pendukung Keputusan Pemilihan Supplier Produk Sepatu Fatimah, Siti; Ardiansah, Temi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.1856

Abstract

The selection of shoe product suppliers is an important process in the shoe industry, which requires careful consideration to ensure price, quality, availability, and delivery. In addition, the ability of suppliers to adapt to changing market demands and the ability to collaborate in the development of new products are also important factors. The problems that arise are uncertainty in assessing supplier quality and performance, difficulty in comparing supplier alternatives objectively, and difficulty in calculating various relevant criteria thoroughly. This study aims to apply a combination of ROC and MOORA weighting methods in determining the selection of shoe product suppliers so that it becomes a recommendation for companies in choosing the right supplier for the sustainability of the business run by the company. The combination of ROC and MOORA can provide deeper and more thorough insight into the alternatives evaluated. Using this approach, decision makers can address some of the weaknesses of each method simultaneously, resulting in more optimal and measurable decisions. The use of a combination of ROC and MOORA can help organizations in achieving their goals in a more efficient and effective manner. The recommendation for the selection of shoe product suppliers shows the ranking results of each supplier based on the final value of the MOORA and ROC calculations, the ranking results show rank 1 with a value of 0.093 obtained by Supplier F, rank 2 with a value of 0.0827 obtained by Supplier A and Supplier E, and rank 3 with a value of 0.0458 obtained by Supplier H.
Sistem Pendukung Keputusan Pemilihan Saham Terbaik Menggunakan Metode SAW dan ROC Pada Subsektor Perbankan Hanny Caroline; Endang Lestari Ruskan
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1861

Abstract

Many investors, especially novice investors, think that from stocks they can get profits and wealth just by following recommendations without knowing what they are investing in and without understanding the risks of losses that can arise in the future. So to avoid losses, novice investors must conduct fundamental analysis before making long-term investment decisions. There are so many types of stocks listed on the Indonesia Stock Exchange (IDX) that investors can choose from and in conducting fundamental analysis there are many criteria (financial ratios) that must be considered as a basis for choosing the best stocks, therefore, the role of information technology is needed to help select the best stocks more quickly and precisely using special methods. Information technology that can help this is a Decision Support System. One method in the decision-making process is Simple Additive Weighting (SAW). The assessment capability in this method is more precise and accurate because it is based on the value of the criteria and the specified weights. The weight value will be determined using the ROC method. This study uses 13 criteria in the process of determining the best stocks, namely EPS, ROE, PER, PBV, DPR, NPM, ROI, ROA, DY, DER, QR, CR, and LDR obtained from the questionnaire results. The alternatives used are stocks from 44 banking subsector companies listed on the Indonesia Stock Exchange (IDX). From the calculations carried out, BBCA stock as A7 with a value of 0.920 is the best stock alternative
Analisis Data Time Series Untuk Prediksi Harga Komoditas Pangan Menggunakan Autoregressive Integrated Moving Average Sihombing, Ester Ivo; Suhendra, Christian Dwi; Marini, Lion Ferdinand
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1863

Abstract

Onions and chilies are two of the many food commodities frequently used by Indonesian people in their daily lives. The high demand in the market leads to price instability, causing prices to fluctuate or remain unstable. This can result in farmers suffering losses when selling their agricultural products. Therefore, forecasting is conducted to predict future prices of onions and chilies. This can provide information on the estimated prices that farmers will set for sale to traders, which is expected to address market price instability. This research aims to obtain the best model from the Autoregressive Integrated Moving Average (ARIMA) for forecasting the prices of onions and chilies in Manokwari Regency in 2024. The data for this study is sourced from the SP2KP (Market and Basic Needs Monitoring System) website, consisting of price data for red onions, garlic, and bird's eye chilies from January 2016 to December 2023. The best ARIMA models based on the smallest AIC values are ARIMA (2,0,0) with an AIC of 1341.784, ARIMA (3,0,0) with an AIC of 1278.688, and ARIMA (1,0,0) with an AIC of 1466.834 for red onions, garlic, and bird's eye chilies respectively, with RMSE values of 7447.06, 3501.71, and 13787.59 respectively. From these models, the predicted prices of the three commodities in 2024 from January to December are as follows: red onions around Rp 50,000/kg, garlic around Rp 40,000/kg, and bird's eye chilies between Rp 50,000 and Rp 70,000/kg
Penerapan Metode Neural Network Berbasis Web Dalam Prediksi Harga Telur Ayam Febiansyah Annaufal Ahnaf Fauzi; Sri Wulandari; Donny Avianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1865

Abstract

The demand for animal protein consumption is increasing in line with the development and growth of the livestock industry. Chicken eggs are one of the choices as a source of protein due to their abundant availability and affordable price. However, Yogyakarta Province experiences unstable egg price fluctuations, as indicated by the imbalance between high demand and limited production. To overcome this challenge, the authors developed the use of the Neural Network Backpropagation method to predict chicken egg prices. The selection of this method is based on its reputation for providing accurate predictions in this case. The implementation of this method resulted in an accuracy rate of 85%, which provides farmers with one of the useful tools to better manage risks and plan their production. This research is expected to make a significant contribution to the livestock industry, by providing farmers with a useful tool to manage risks and plan their production activities. In addition, this research is also expected to provide a better understanding of market behavior for stakeholders in Yogyakarta Province and the wider community. Thus, it is expected that this effort will not only improve the sustainability of the local economy but will also advance the livestock industry as a whole. With the results of this study, farmers are expected to optimize their strategies in adjusting production to the fluctuating market demand. In addition, stakeholders in Yogyakarta Province can use this information to develop more effective policies to support the growth of the livestock sector, especially in chicken egg farming
Sistem Pengendalian Persediaan Stok Obat dengan Menggunakan Metode Analisis Always Better Control dan Metode Economic Order Quantity Pada Apotek Shofika Adilya; Fitriani Muttakin; Angraini
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1866

Abstract

XYZ Pharmacy is a pharmacy that specializes in the sale of prescription medications and exclusively accepts prescriptions from licensed physicians. Problems that arise include determining which drugs should be prioritized to reduce production costs which result in unstable drug stock supplies, and frequent excesses and shortages of drug stock due to delays in drug orders from suppliers. Therefore, it is necessary to apply a technique that makes it easier to control drug stock at XYZ Pharmacy, which aims to make it easier for XYZ Pharmacy to determine which drugs should be prioritized and manage drug stock control in the pharmacy. To overcome these problems effectively. ABC analysis of drugs in group A shows that there are 7 types of drugs which contribute 35.00% of total drug use, and generate total income of 67%. In group B there are 6 types of drugs which represent 30.00% of total drug use and generate total income of 23%. Finally, group C consists of 7 types of drugs which cover 35.00% of total drug use, and generate total revenue of 11%. Using the EOQ method, analysis was carried out on 7 types of drugs in group A, obtaining various optimal order quantities EOQ. The highest EOQ was 90.64 or 90 items for OB2 drugs, while the lowest EOQ was 43.29 or 43 items for OB19. Each type of drug has a range of 1-4 Safety Stock, and the Reorder Point for each type of drug varies in terms of the number of units needed.
Implementasi Algoritma Selection Sort dan First Come First Served Dalam Sistem Reservasi Paket Wisata Tito Rizki Purnomo; Maimunah, Maimunah; Pristi Sukmasetya
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1868

Abstract

Candirejo Village is located in Borobudur District, also known as Candirejo Tourist Village. KOPERASI WISATA was established for the purpose of managing tourist destinations at the village.One of the tourism destination is named Nagari Wayang Kertas which located at Dusun Sangen. All the tourist comes to the village have to go through process from the thirds party which called KOPERASI WISATA. Those causes the arrival process innefective because they can not directly contacting the owner of the tourism destination. Solving this problem, designing a web-based reservation system for Nagari Wayang Kertas  facilitating the booking process would improve the time efficiency required for reservations. The methods of data collecting in this research includes observation, interviews, and literature review. The software development process itself using the Rapid Application Development (RAD) method due to its high success rate in software development. The implemented algorithms which used in this research are Selection Sort and First Come First Served maximizing the efficiency of the reservation process for visitors. Before being tested and implemented to the users, the testing system uses the black-box testing method. The results of the testing method showing that the function of the designed system works and operates as expected and needed
Analisis Perbandingan KNN, SVM, Decision Tree dan Regresi Logistik Untuk Klasifikasi Obesitas Multi Kelas Utiarahman, Siti Andini; A. Mulawati M. Pratama
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1871

Abstract

Obesity has become a concerning global health issue, with continuously increasing prevalence. Early identification and accurate classification of obesity are crucial for implementing appropriate prevention and treatment strategies. This study aims to analyze and compare the performance of four popular classification algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, and Logistic Regression, in performing multi-class obesity classification based on Body Mass Index (BMI)  according to World Health Organization (WHO) standards. Using a dataset reflecting population diversity, this research evaluates the ability of each algorithm to classify obesity into several categories, such as normal, overweight, obesity grade 1, obesity grade 2, and obesity grade 3. The study utilizes 2.111 records with 17 attributes. Results indicate that the Decision Tree Algorithm outperforms other algorithms, achieving an accuracy of 99.3%, precision of 0.97-1.00, recall of 0.98-1.00, and f1-score of 0.98-1.00. KNN follows with an accuracy of 99.0%, precision of 0.98-1.00, recall of 0.98-1.00 and f1-score of 0.98-1.00. meanwhile, the Logistic Regression algorithm achieves an accuracy of 98%, precision of 0.95-1.00, recall of 0.95-1.00, and f1-score of 0.95-1.00. SVM demontrates slightly lower performance, although still showing overall good results with an accuracy of 96.6%, precision of 0.90-0.99, recall of 0.94-1.00, and f1-score of 0.93-0.99..
Enhanced Facial Expression Recognition Through a Hybrid Deep Learning Approach Combining ResNet50 and ResNet34 Models Auliana, Sigit; Mahrojah, Siti; Aryono, Gagah Dwiki Putra
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1874

Abstract

Recognizing facial expressions is a critical aspect of computer vision and human-computer interaction. It facilitates the interpretation of human emotions from facial images, aiding in applications such as affective computing, social robotics, and psychological research. In this work, we propose using hybrid deep learning models, ResNet50 and ResNet34, for facial expression classification. These models, pre-trained on large-scale datasets, demonstrate exceptional feature extraction capabilities and have achieved excellent performance in various computer vision tasks. Our approach begins with the collection and preprocessing of a labeled facial expression dataset. The collected data undergoes face detection, alignment, and normalization to ensure consistency and reduce noise. After preprocessing, the dataset is divided into training, validation, and testing sets. We fine-tune the ResNet50 and ResNet34 models on the training set, employing transfer learning to adapt the pre-trained models specifically for the facial expression recognition task. Optimization techniques such as SGDM, ADAM, and RMSprop are used to update the models' parameters and minimize the categorical cross-entropy loss function. The trained models are evaluated on the validation set, achieving an accuracy of 98.19%. Subsequently, the models are tested on unseen facial images to assess their generalization capabilities. This proposed approach aims to deliver accurate and robust facial expression classification, thereby advancing emotion analysis and human-computer interaction systems.
Multi-Domain Medical Image Enhancement Through Fuzzy and Regression Neural Network Approach Auliana, Sigit; Nur Janah, Meishi; Gagah Dwiki Putra Aryono
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1875

Abstract

Medical image processing has heralded a significant transformation in contemporary medical science, offering the promise of diagnosing, treating, and curing patients while minimizing adverse effects. By leveraging medical imaging, physicians gain the ability to visualize internal structures without invasive procedures. Moreover, this technology contributes to our understanding of neurobiology and human behavior, with brain imaging aiding investigations into addiction mechanisms. Interdisciplinary collaboration among biologists, chemists, and physicists is facilitated by medical imaging, with resultant technologies finding applications across various fields. This study focuses on enhancing medical images in both frequency and time domains. Contrast enhancement is achieved through local transformation histogram techniques, followed by overall enhancement using a Fuzzy-Neural approach. The proposed methodology is implemented using MATLAB 2018b. The findings emphasize the efficacy of the proposed technique in improving image quality for both MR and Selenography images. Its outstanding performance, marked by a higher PSNR (32.96) and a lower MSE (20.04), indicates its potential for more precise and dependable image enhancement compared to current methods.

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