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EXPLORER
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27744647     DOI : https://doi.org/10.47065/explorer.v2i1.148
Core Subject : Science,
Computer Science & IT
EXPLORER Journal of Computer Science and Information Technology is a scientific journal published by the FKPT (Forum Kerjasama Pendidikan Tinggi). This journal contains scientific papers from Academics, Researchers, and Practitioners about research on Computer Science and Information Technology. EXPLORER Journal of Computer Science and Information Technology is published twice a year in January and July. The paper is an original script and has a research base on Computer Science and Information Technology. The scope of the paper includes several studies but is not limited to the study Artificial Intelligence, Computer Graphics and Animation, Image Processing, Cryptography, Computer Network Security, Modelling and Simulation, Multimedia, Computer Architecture Design, Computer Vision and Robotics, Parallel and Distributed Computing, Operating System, Information System, Mobile Computing, Natural Language Processing, Data Mining, Machine Learning, Expert System and Geographical Information System. Thus, we invite Academics, Researchers, and Practitioners to participate in submitting their work to this journal.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 65 Documents
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Penggunaan Algoritma Naïve Bayes Untuk Menentukan Pemberian Kredit Pada Koperasi Desa Ika Nurjanah; Jamilah Karaman; Ida Widaningrum; Dyah Mustikasari; Sucipto Sucipto
Explorer Vol 3 No 2 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v3i2.766

Abstract

Giving credit to customers is a routine activity carried out by a cooperative, as happened in the Subur Sari Forest Village Community Institution Cooperative (LMDH) in Pudak Wetan Village, Ponorogo Regency. Non-performing or bad loans often occur due to a lack of thorough analysis in the credit granting process. This happens because the management is less careful in determining which applicants are eligible for loans. Therefore, customer eligibility analysis is fundamental in determining whether a customer is eligible or not to get a loan. One way to determine creditworthiness is to use the Naïve Bayes algorithm. This study aims to apply data mining methods to classify eligible, and ineligible customers based on historical customer data in the past then used to predict the feasibility of future customers using the Naïve Bayes algorithm. The results of testing the credit classification system using the black box stated that the system was able to run according to the algorithm and could determine whether or not the customer deserved credit.
Pengelompokan Bidang Usaha Terhadap Bantuan Produktif Usaha Mikro (BPUM) Berdasarkan Wilayah Deli Serdang Menggunakan Metode Clustering K-Means (Studi Kasus: Dinas Koperasi Dan UMKM Kabupaten Deli Serdang) Tiara Jelita; Relita Buaton; Magdalena Simanjuntak
Explorer Vol 3 No 2 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v3i2.783

Abstract

Micro Business Productive Assistance is a program that is assistance from the government to MSME workers throughout Indonesia. Every year, MSMEs can receive this assistance, without exception for those who have received it in previous years. The Office of Cooperatives and MSMEs of Deli Serdang Regency is a regional apparatus in North Sumatra Province which has the main task of carrying out government affairs in the field of cooperatives and small businesses including saving and loan business permits, empowerment and development of small businesses. Micro, Small and Medium Enterprises (MSMEs) are individual business entities which contributed significantly to increasing exports, increasing and equalizing income, forming national products and expanding employment opportunities. Based on these conditions, the authors provide a solution that needs to be built a clustering that can classify fields in each business owned by the community, because not all types of business fields in the community will receive this assistance, including agriculture and animal husbandry. Grouping data can apply the data mining process with the K-Means Algorithm clustering method which is a process of processing very large amounts of data using statistical methods, mathematics, and utilizing Artificial Intelligence technology to produce a group of data. By utilizing the data mining process using the clustering method, it is hoped that clustering can solve the problem of grouping business fields owned by the community. From the test results with 1004 data, which was carried out with MATLAB, it was found that group 1 had 383 data, group 2 had 261 data and group 3 had 360 data. Meanwhile, based on the results of the trial with RapidMiner, it was found that group 1 had 371 data, group 2 had 281 data and group 3 had 352 data.
Pengelompokan Data Mining Penerimaan Bantuan Pangan Non Tunai (BPNT) Menggunakan Metode Clustering (Studi Kasus : Kantor Desa Payabakung Hamparan Perak) Fany Juliawati; Relita Buaton; Rusmin Saragih
Explorer Vol 3 No 2 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v3i2.793

Abstract

Poverty is a problem that is often faced by various countries in the world, including Indonesia. In an effort to overcome poverty and increase people's access to food, in 2017 the Government gradually created a program that was formed to reduce the burden on the community in meeting basic needs, with the Non-Cash Food Assistance Program (BPNT). The problem is that the assistance provided has not been distributed on target / the distribution of assistance has not been objective, due to limited data and information obtained regarding families receiving BPNT assistance, so that families who should be entitled to receive assistance cannot receive assistance due to limited data available. Therefore, the village office is required to record again the families who are entitled to receive BPNT assistance with the existing criteria. The solution offered is to create a system of k-means that displays the clustering results of recipients of Non-Cash Food Assistance, by utilizing a number of data owned by the agency, it can be grouped using data mining technology. The benefit is that data mining can help agencies gain knowledge. by processing existing BPNT beneficiary data. The use of data mining techniques in grouping BPNT recipients is expected to be useful in facilitating the process of searching system data, which was previously still manual. The data group for recipients of Non-Cash Food Assistance (BPNT) in the work group (X) are private employees, for the income group (Y) are 1,400,001 – 1,700,000 and in the home status group (Z) are self-owned homes, and Centroid 2 ( 1,552,861.44), the data group for recipients of Non-Cash Food Assistance (BPNT) in the occupation group (X) is Plantation, for the income group (Y) is 800,001 – 1,100,000 and in the house status group (Z) is Owned house, and Centroid3 (4,592,351.64) data group for recipients of Non-Cash Food Assistance (BPNT) in the occupation group (X) is Labor, for the income group (Y) is 500,001 – 800,000 and in the house status group (Z) is Rent house.
Klasifikasi Data Penduduk Pada Pemilihan Umum Di Kota Binjai Menggunakan Algoritma K-Means (Studi Kasus : KPU Kota Binjai) Windy Indah Sary Sinaga; Relita Buaton; Hermansyah Sembiring
Explorer Vol 3 No 2 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v3i2.794

Abstract

Population growth is something that continues in an environment both in rural and urban areas. The rapidly increasing number of residents must be re-recorded in a government agency. Likewise, the Binjai City KPU Office must re-record population data, especially residents in the city of Binjai who have the right to carry out the General Election in 2024 by involving the community that has been previously recorded. Problems were also found with data on residents who had moved domiciles but their personal data had already been recorded for general elections in 2024. With that, data collection had to be re-done to select population data so as to produce a new population data status so that data was not found that did not match what it should be. By observing the problems above Data Mining with the Clustering method is very appropriate to be used to generate knowledge of new population data groups to carry out general elections at the KPU Binjai, using the MATLAB application is also very appropriate to choose in this problem so that it can produce output from data mining that can be used in future decision making. This study aims to process data to produce population data in the city of Binjai in the implementation of general elections, implement a system so that it can classify new population data in the middle of old population data and design data grouping in determining population data groups based on criteria conditions at the KPU Office in Binjai city. By using the clustering method that has been used to process population data at general elections in the city of Binjai, it can produce new information from 1000 data that has been tested. From 1000 population data for general elections in Binjai City, 3 clusters are obtained with the results of 7 tests where cluster 1 totals 225 data, cluster 2 has 436 data and cluster 3 has 339 data.
Rancang Bangun Sistem Absensi Berbasis Website di SMK Muhammadiyah 3 Dolopo Karaman, Jamilah; Gunawan, Putri Miya; Firdhossiah, Shailatul; Fitriani, Lely Mustikasari Mahardhika; Sucipto, Sucipto; Indriati, Rini
Explorer Vol 4 No 1 (2024): January 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v4i1.818

Abstract

Attendance is an important part of collecting attendance data at an event. The data serves as a tool for evaluation, accountability, and event development. SMK Muhammadiyah 3 Dolopo, a vocational high school in Madiun Regency, East Java, is one of the educational institutions that requires a good attendance system. The school aims to excel in the field of technology and information. To achieve this goal, improving the quality of learning and service to students, including in terms of absenteeism, is needed. Currently, SMK Muhammadiyah 3 Dolopo still uses a manual attendance system which has several disadvantages, such as time-consuming, error-prone, easy to fake, difficult to archive, and less flexible. The use of attendance system can be done quickly and accurately, anywhere and anytime. The use of the Attendance Information System is an effective solution in monitoring and managing student attendance accurately and efficiently. Research shows that this system helps maximize learning time by ensuring timely student attendance. The results showed that the adoption of this technology has the potential to improve the overall quality and effectiveness of learning
Analisis Existing Convolutional Neural Network Untuk Klasifikasi Usia Pengunjung Rumah Sakit: Studi Kasus Pemantauan Anak dan Dewasa Harahap, Herlina; Rahman, Sayuti; Zen, Muhammad; Suriati, Suriati
Explorer Vol 4 No 1 (2024): January 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v4i1.881

Abstract

The purpose of this study is to examine the Convolutional Neural Network (CNN) model for classifying the age groups of hospital visits, both children and adults. Hospitals serve as treatment facilities for a variety of ailments caused by viruses, germs, car accidents, and other factors. Children are not permitted to visit the hospital due to hurdles to patient comfort as well as hazards associated with immunity and trauma to children. As a result, a digital strategy is required to monitor the presence of youngsters in the hospital setting. The notion of computer vision and the Convolutional Neural Network (CNN) are employed in this study to attain this goal. The dataset utilized is All-Age-Faces (AAF), which includes photos of human faces ranging in age from 2 to 80 years. To categorize visitors into children or adults, two CNN architectures, ResNet and SqueezeNet, are used with fine-tuning (FT) and full retraining (FR) approaches. The accuracy of FR-ResNet was 97.22%, beating the accuracy of the previous research FT-SqueezeNet, which was 93.09%, better to 4.13%. This study confirmed that the use of CNN, namely the FR-ResNet technique, was effective in accurately categorizing the age of hospital visits. Controlling children's access to hospital areas can help reduce the danger of illness transmission.
Proteksi Keamanan Data dengan Menerapkan Algoritma Bacon Cipher dan ROT128 Rahmatsyah, Indra; Siregar, Yunita Sari; Khairunnisa, Khairunnisa
Explorer Vol 4 No 1 (2024): January 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v4i1.1099

Abstract

The data storage and information exchange activities digitally have risks and of course must be accompanied by information security. Various ways are done to protect data such as hiding data with steganography techniques or by encoding data with cryptographic techniques. If in general the steganography technique is done by inserting data into image, audio or video media, but in this study the data will be inserted or hidden into a text file as a media (cover message) to hide the data. While data security with cryptographic techniques is done by changing the data to be kept secret (plaintext) into encoded data (ciphertext) so that the data cannot be understood by people who do not have legality in the data. Therefore, with this method of securing research files, it produces a test of blackbox testing of encryption and description and extraction. Where is the result of the encryption on the encrypted text file using the ROT128 algorithm with a time of 1.82ms and where is the result of the description on the text file producing a decrypted text file using the ROT128 algorithm with a time of 1.47ms. After carrying out the extraction process on the resulting text file description using the Bacon Cipher algorithm, it will produce the original text with a time of 13.95ms
Penerapan Multi-Layer Perceptron untuk Mengklasifikasi Penduduk Kurang Mampu Gulo, Senang Hati; Lubis, Andre Hasudungan
Explorer Vol 4 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v4i2.1146

Abstract

The classification of the less capable population in Afulu Sub-district is currently reliant on a manual system, resulting in prolonged processing times. To address this issue, this research endeavors to develop a practical application for the classification of population data, with the primary objective of expediting the processing of population data in Afulu Sub-district. The study will focus on nine villages within the sub-district, encompassing a total population of 11,722 individuals, with a sample size of 386. The present study utilizes the Multilayer Perceptron, a classical algorithm that continues to be the most widely employed method in numerous researches. The findings of the present study indicate that out of the total sample size, 152 individuals were classified as capable, 86 individuals were classified as moderately capable, and a substantial number of 148 individuals were classified as less capable. The classification results were evaluated using a confusion matrix. The 3-5-1 architecture, comprising of 3 input layers, 5 hidden layers, and 1 output layer, was found to be the most superior. This architecture demonstrated an accuracy value of 96.9%, a recall value of 92%, a precision value of 98.5%, and an F-score value of 94.9%. A detailed elucidation of the parameters employed, the formulas utilized, and several computations performed are explained further.
Perbandingan Metode AHP dan ANP Pemilihan Presiden Tahun 2024 Generasi Milenial Politeknik Negeri Cilacap Syafirullah, Lutfi; Maharrani, Ratih Hafsarah; Bahroni, Isa; Vikasari, Cahya
Explorer Vol 4 No 1 (2024): January 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v4i1.1151

Abstract

The Presidential Election is one of the key elements in democracy which is regulated by the 1945 Constitution of the Republic of Indonesia (UUD 1945). General elections (elections) in Indonesia are held every five years, and the people have the right to vote to elect a presidential candidate who they consider worthy of leading the country during the next term of office. The Presidential election process involves various stages, including nomination by political parties or individuals, political campaigns, debates between candidates, and the voting or election stage. The presidential election (presidential election) is held simultaneously with the legislative election. It is important to note that elections are the foundation for democratic governance, ensuring the active participation of the people in determining the direction and leadership of the country. The Presidential Election in Indonesia is a manifestation of the implementation of democratic principles in the country's political system. The AHP (Analytic Hierarchy Process) method and the ANP (Analytic Network Process) method are methods that can be used in making decisions. A method developed by mathematician Thomas L. Saaty to help solve very complex problems by breaking down various decisions into several smaller criteria. The AHP and ANP methods can overcome assessments based on subjective viewpoints and personal preferences, and transform them into weights with consistent and rational values. Super Decisions is a tool developed specifically to support AHP and ANP calculation applications. Super Decisions provides useful tools in analyzing and processing comparison tables, performing consistency calculations, and assisting in AHP and ANP based decision making.
Analisis Sentimen Ulasan Aplikasi Bank Digital Menggunakan Algoritma Naïve Bayes Adelia Irawan, Febby; Rialdy Atmadja, Aldy; Wahana, Agung
Explorer Vol 4 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v4i2.1181

Abstract

Bidang perbankan merupakan salah satu yang berkembang dan mengikuti tren digitalisasi. Adanya bank digital merupakan inovasi yang dilakukan pada bidang perbankan dalam memberikan pelayanan dengan menggunakan media elektronik atau digital. Teknologi yang dikembangkan memungkinkan pengguna hanya cukup mengakses transaksi dalam suatu aplikasi dengan bermodalkan smartphone yang didistribusikan melalui Google Playstore. Ulasan-ulasan pengguna (review) pada Google Playstore ini tersedia untuk membantu meningkatkan performa dari aplikasi dan menjadi landasan bagi perusahaan dalam mengembangkan aplikasi perbankan. Akan tetapi, terdapat kendala jika banyaknya ulasan dan sulit untuk memilah dan mengolahnya secara manual sehingga diperlukan analisis sentimen ulasan pengguna pada aplikasi-aplikasi bank digital. Pada penelitian ini analisis sentimen dilakukan dengan menggunakan algoritma Naïve Bayes. Adapun pendekatan metode yang dilakukan dengan menggunakan CRISP-DM sebagai standar yang umum dalam melakukan riset data mining. Hasil dalam penelitian ini menunjukkan bahwa penerapan model klasifikasi dengan menggunakan Algoritma Naïve Bayes dengan data ulasan menghasilkan 46% ulasan positif dan 54% ulasan negatif. Selain itu, nilai akurasi tertinggi dari kinerja algoritma Naïve Bayes dengan menggunakan pembagian data training dan testing dengan persentase 70:30 menghasilkan akurasi yang optimal mencapai 89%.

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