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Muhammad Taufiq Nuruzzaman
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JISKa (Jurnal Informatika Sunan Kalijaga)
Published by Universitas Islam Negeri Sunan Kalijaga Yogyakarta
ISSN : 25275836     EISSN : 25280074     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Library & Information Science
JISKa (Jurnal Informatika Sunan Kalijaga) adalah jurnal yang mencoba untuk mempelajari dan mengembangkan konsep Integrasi dan Interkoneksi Agama dan Informatika yang diterbitkan oleh Departemen Teknik Informasi UIN Sunan Kalijaga Yogyakarta. JISKa menyediakan forum bagi para dosen, peneliti, mahasiswa dan praktisi untuk menerbitkan artikel penelitiannya, mengkaji artikel dari para kontributor, dan teknologi baru yang berkaitan dengan informatika dari berbagai disiplin ilmu
Arjuna Subject : -
Articles 231 Documents
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Penentuan Kelayakan Masyarakat Miskin Penerima Bantuan Menggunakan Metode Naïve Bayes (Studi Kasus: Kabupaten Penajam Paser Utara) Nur Madia; Anindita Septiarini; Heliza Rahmania Hatta; Hamdani Hamdani; Masna Wati
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 8 No. 1 (2023): Januari 2023
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2023.8.1.36-49

Abstract

Contents Poverty is the inability to meet the necessities of life, such as food, clothing, and shelter. The poor have an average monthly per capita expenditure below the poverty line. The case of poverty in Indonesia is still unresolved; the Government continues to try to give the best to the entire community so that the problem of poverty can at least continue to decrease. One form of government concern for the poor is the assistance program provided to the poor. This study will classify based on data from the North Penajam Paser (PPU) community obtained from the results of the National Socio-Economic Survey (Susenas) to know how the Naïve Bayes method is in determining the eligibility of the poor recipients of assistance. Based on the research that has been carried out, a system for determining the poor recipients of assistance is produced, where the test results get the highest accuracy in the third scenario, namely 60% or 328 training data and 40% or 218 test data, where the accuracy obtained is 77.98%.
Analisis Perbandingan Metode Pendukung Keputusan Pemilihan Kos Mahasiswa di Pontianak Noerul Hanin; David Jordy Dhandio; Della Zaria
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 8 No. 1 (2023): Januari 2023
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2023.8.1.50-65

Abstract

The existence of boarding houses in public spaces is highly expected by the community, especially migrants such as students who need a temporary house in oversea areas. In Pontianak, especially around Tanjungpura University, there are many boarding houses that offer various facilities with various rental prices. Thus, decision support analysis is needed to choose a good boarding house for students around Tanjungpura University. In this study, two decision support system methods were selected, those are SAW and TOPSIS. These two methods were chosen because they have uncomplicated calculations, but are capable to produce good decisions. A comparison of the two methods was carried out to find out differences in results and calculation concepts to choose boarding houses for students in Pontianak. Data that was used for the trial were 10 alternative boarding houses located around the university. Based on trial results, the best boarding house obtained using SAW and TOPSIS methods is Yoga Kost.
Penerapan Algoritma K-Means untuk Klasterisasi Penduduk Miskin pada Kota Pagar Alam Febriansyah Febriansyah; Siti Muntari
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 8 No. 1 (2023): Januari 2023
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2023.8.1.66-77

Abstract

The purpose of this study was to obtain a poverty data cluster in Pagar Alam City. The data collection of beneficiaries of the Program Keluarga Harapan (PKH) is not correct, the provision of assistance only pays attention to the criteria for poverty in general, so there are still many poor people who feel more deserving of PKH assistance. To overcome the problem of PKH recipients, it is necessary to cluster the community into various levels, so that the government can know the level of poverty of the community and can provide PKH assistance appropriately. The methods used in this study are CRISP-DM and the K-Means clustering algorithm. The attributes used are Identity Number, Name, Family Family Card Number, Poverty Rate, Pregnant Women, Early Childhood, Elementary School, Junior High School, Senior High School, Elderly, and Family Hope Program Recipient Group. This clustering process produced three clusters, namely cluster_0 as many as 156 people, cluster_1 as many as 82 people, and cluster_2 as many as 233 people. Furthermore, it was developed into a system with the Rapid Application Development (RAD) system development method. Thus producing a K-Means algorithm system to classify the poor in Pagar Alam City. The system test method uses black box testing with the alpha method and obtained database test results with a value of 4, interfaces with a value of 4, functionality of 4.42, and algorithms with a value of 4. In the testing process with UAT, in the system aspect got 87% of users agreed, in the user aspect 86% agreed, and in the interaction aspect 87% of users agreed. So it can be concluded that this system is worth using.
Analisa Deteksi dan Pengenalan Wajah pada Citra dengan Permasalahan Visual Verry Noval Kristanto; Imam Riadi; Yudi Prayudi
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 8 No. 1 (2023): Januari 2023
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2023.8.1.78-89

Abstract

Facial recognition is a significant part of criminal investigations because it may be used to identify the offender when the criminal's face is consciously or accidentally recorded on camera or video. However, a majority of these digital photos have poor picture quality, which complicates and lengthens the process of identifying a face image. The purpose of this study is to discover and identify faces in these low-quality digital photographs using the Principal Component Analysis (PCA) and Linear  Discriminant Analysis (LDA) face identification method and the Viola-Jones face recognition method. The success percentage for the labeled face in the wild (LFW) dataset is 63.33%, whereas the success rate for face94 is 46.66%, while LDA is only a maximum of 20% on noise and brightness. One of the names and faces from the dataset is displayed by the facial recognition system. The brightness of the image, where the facial item is located, and any new objects that have entered the scene have an impact on the success rate.
Analisis Klasifikasi Broken Home pada Anak Menggunakan Metode Naïve Bayes Classifier Supiyandi Supiyandi; Supiyandi Supiyandi; Almanna Hussein; Irwan Gunawan; William Lutfi Rahman Harjo
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 8 No. 2 (2023): Mei 2023
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2023.8.2.90-101

Abstract

Broken home is a term that defines a situation in a family where most people handle no harmony, happiness, or peace. The impact of a broken home on a depressed family on children who can experience mental, emotional, and behavioral changes that are uncontrolled and undirected. Therefore, a classification is needed to categorize a child in a family as a broken home or not. The classification process will apply the Naïve Bayes Classifier classification method by taking into account the factors that refer to the statement that a child is called a broken home. With this classification, it is hoped that it can help know what and how a broken home child can be called a broken home and with this classification, it is expected that parents can minimize broken homes in children in the future by paying attention to the determining factors.
Prediksi Kategori Kelulusan Mahasiswa Menggunakan Metode Regresi Logistik Multinomial Rafika Syahranita; Suhartono Suhartono; Syahiduz Zaman
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 8 No. 2 (2023): Mei 2023
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2023.8.2.102-111

Abstract

Students must meet certain goals to earn a degree but can extend their time at university or drop out (DO). The problem of dropping out of students has become an important issue for tertiary institutions to ensure the success or graduation of students and reduce dropouts. DO can affect the accreditation of the tertiary institution. The quality of higher education institutions in Indonesia is measured based on accreditation from the National Accreditation Board for Higher Education or BAN-PT. One of the main standards measured is the Quality of Students and Graduates. The quality of educational accreditation is measured by the percentage of student graduation and the university's strategy to retain students. To predict student graduation based on graduation time categories, researchers collected academic data from students in 2012-2018 at the Informatics Engineering Study Program, State Islamic University of Maulana Malik Ibrahim Malang. The variables used as predictors are gender, type of entry pathway, and grade point average from semesters one to six. The resulting model was evaluated to obtain an accuracy value of 85.5%, a precision of 78.5%, a recall of 93.9%, and a micro f1-score of 89.8%. An accuracy value of 85.5% indicates that the system can classify properly using the logistic regression model.
Klasifikasi Ulasan Fasilitas Publik Menggunakan Metode Naïve Bayes dengan Seleksi Fitur Chi-Square Adhitya Prayoga Permana; Totok Chamidy; Cahyo Crysdian
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 8 No. 2 (2023): Mei 2023
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2023.8.2.112-124

Abstract

Government builds public facilities to support the needs of the community. The use of these public facilities needs to be re-evaluated, and one way to do it is through community response. Google Maps is one platform that receives the most responses from the community about location. Google Maps Reviews allow us to see how the public reacts to a location. Naïve Bayes method is used for classification in this study because it is one of the simple methods in machine learning that can be easily applied to several experiments conducted by the author. In the classification process, reviews produce many features that will be calculated based on their class. More features generated, more features processed too in the system. Chi-Square feature selection will be used to reduce features that have low dependence on the system. In this study, performance values will be calculated based on the experimental use of feature ratios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%. The results show that the use of 10% Chi-Square features produces the best performance, with an accuracy rate of 86.94%, precision of 80.42%, recall of 80.42%, and f-measure of 80.42%.
Sistem Pengukuran Kualitas Media pada Larva BSF (Black Soldier Fly) Berbasis Internet of Things Menggunakan Metode Naive Bayes Mohammad Faisal Fajar Fadilah; Ajib Hanani; Totok Chamidy
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 8 No. 2 (2023): Mei 2023
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2023.8.2.125-139

Abstract

Piles of waste increase in line with population growth and consumption patterns. The concept of bioconversion using black soldier fly larvae can solve the problem of organic waste management. From these problems, an application of Internet of Things technology is needed. The system implemented aims to allow the system to find out how much accuracy, precision, and recall are in making decisions on media quality values using the Naive Bayes method. The main feature of this Naive Bayes Classifier is the very strong assumption of the independence of each condition or event. From the research results, the system has been successfully built according to the research design, as well as the goals that have been fulfilled in completing the development of the smart maggot. Several sensors used in this study were tested so that sensor performance could be determined by finding the average error value. Three parameters are measured; namely, the temperature obtained an average error of 1.6%, air humidity obtained an average error of 2.03%, and soil moisture obtained an average error of 2.7%. By measuring using Python, the Confusion Matrix is obtained so that the test results from the calculation of the Naive Bayes method can find the data in the form of accuracy, precision, and recall. Accuracy percentage results obtained 92%, precision percentage average results obtained 93%, and recall percentage average results obtained 92%. The conclusion shows the results of the system's accuracy obtained have worked well.
Evaluasi Penerimaan Masyarakat Terhadap Aplikasi Telemedicine pada Masa Pandemi COVID-19 Muhammad Reza Velayani; Muhammad Taufiq Nuruzzaman; Agung Fatwanto; Bambang Sugiantoro
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 8 No. 2 (2023): Mei 2023
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2023.8.2.140-153

Abstract

Telemedicine is a technology that provides benefits during the COVID-19 pandemic, which has been going on for more than two years. However, we have never conducted an evaluation or assessment of Telemedicine applications. In this study, we tried to look at people's acceptance of using the application, and 104 participants became respondents. This study uses the TAM (Technology Acceptance Model) method, which this method measures the influence between variables. TAM has five indicator variables: Perceived Ease of Use, Perceived Usefulness, Attitude Toward Using, Behavioral Intention, and Actual Usage. In this study, four models were made with three comparison models; then, for the results of testing the relationship between variables, three hypotheses are always accepted in each model, namely Perceived Ease of Use with Perceived Usefulness, Perception Ease of Use with Attitude Toward Using, and Behavioral Intention with Actual Usage. Then the two hypotheses always rejected in each model are Perceived Usefulness with Attitude Toward Using, Attitude Toward Using with Behavioral Intention. Then there is one hypothesis for each model that is refused, namely Perceived Usefulness with Behavioral Intention, Perceived Usefulness with Actual Usage, Perceived Ease of Use with Behavioral Intention, and Perceived Ease of Use with Actual Usage.
Penerapan Naïve Bayes pada Potensi Akademik Siswa SD Negeri 5 Singakerta Ni Kadek Winda Patrianingsih; I Kadek Arya Sugianta
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 8 No. 2 (2023): Mei 2023
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2023.8.2.154-163

Abstract

Student potential cannot only be measured based on the result of academic scores, and many things influence student academic determination. The purpose of this research is to prove that students' potential is influenced by many things, such as character, academic activity, socioeconomic status, and distance of residence. By using the naïve Bayes method and testing with the confusion matrix, it will give results for this research. The data is from V-grade students at SD Negeri 5 Singakerta, with 120 students assisted by the homeroom teacher. Based on the results of the tests that have been carried out using a data sample of 10 students and 1 data using the Naïve Bayes, it is obtained that students have academic potential, and the results with the confusion matrix are accuracy of 75%, precision of 81%, and recall of 89%. In this case, it can be concluded that the academic potential of students can not only be measured based on the results of the final grade, but many other factors have an effect, the application of the Naïve Bayes in students' academic potential is appropriate to use.

Page 16 of 24 | Total Record : 231

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