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All Journal TEKNIK INFORMATIKA Syntax Jurnal Informatika Jurnal Ilmu Komputer dan Agri-Informatika SITEKIN: Jurnal Sains, Teknologi dan Industri CESS (Journal of Computer Engineering, System and Science) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Informatika Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer Zonasi: Jurnal Sistem Informasi Journal of Applied Engineering and Technological Science (JAETS) Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer Information System Journal (INFOS) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal UNITEK Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Indonesia Jurnal Informatika: Jurnal Pengembangan IT Jurnal Komtika (Komputasi dan Informatika)
Fadhilah Syafria
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Application of K-Means Algorithm on Clustering Recipients of Non-Cash Food Assistance (NCFA) Said Nanda Saputra; Elin Haerani; Jasril Jasril; Lola Oktavia; Fadhilah Syafria
CESS (Journal of Computer Engineering, System and Science) Vol 8, No 2 (2023): July 2023
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v8i2.48026

Abstract

Persoalan Kemiskinan pada berbagai daerah Indonesia menjadi fokus perhatian. Program BPNT (Bantuan Pangan Non Tunai) bermaksud memangkas biaya pangan dan membagikan gizi yang sepadan terhadap KPM (Keluarga Penerima Manfaat). Penelitian ini menerapkan algoritma K-Means untuk menganalisis pola karakteristik penerima BPNT di Pekanbaru. Data yang digunakan berasal dari penelitian sebelumnya oleh Firza Syahputra dan dari Dinas Sosial Kota Pekanbaru tahun 2020-2021 dengan 732 data dan 41 parameter. Penerapan K-Means dilakukan melalui Google Colab. Melalui data mining dan metode clustering, ditemukan dua klaster dengan 666 data dalam klaster 1 dan 16 data dalam klaster 2. Evaluasi menggunakan Silhouette Score menunjukkan hasil yang baik, dengan nilai 0.9169796594018274. Penelitian ini berpotensi membantu pemerintah dalam mengambil keputusan yang efektif selama penyebaran bantuan pangan non tunai kepada rakyat yang membutuhkan. Dengan demikian, algoritma K-Means Clustering dapat mengidentifikasi pola karakteristik penerima BPNT dan membedakan kelompok yang layak dan tidak layak menerima bantuan.Poverty issues in various parts of Indonesia are the focus of attention. The NCFA (Non-Cash Food Assistance) program's purpose are to lower food consumption and give Beneficiary Families (BF) a healthy diet. The k-means technique use in this study to assess the distinctive patterns of NCFA grantees in Pekanbaru. The data used comes from previous research by Firza Syahputra and from Social Affairs Office Pekanbaru in 2020-2021 with 732 data and 41 parameters. The application of k-means is done through Google Colab. Through data mining and clustering methods, two clusters were found with 666 data in cluster 1 and 16 data in cluster 2. Evaluation using Silhouette Score showed good results, with a value of 0.9169796594018274. This research has the potential to assist the government in making effective decisions in distributing non-cash food help people in need. For the result, the k-means Clustering technique is able to recognize the traits of NCFA recipients and identify groups that are and are not eligible for aid.
Prediksi Jumlah Perceraian Menggunakan Metode Extreme Learning Machine (ELM) Mawadda Warohma; Elvia Budianita; Fadhilah Syafria; Iis Afrianty
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3581

Abstract

Divorce lawsuits have considerably increased in frequency in Indonesia. According to a Statistics Indonesia estimate, there were 447,743 divorce cases in 2021, up 53.50% from the 291,677 instances that were reported in 2020. According to data from the Pekanbaru Religious Court's Public Relations, there were 1,756 divorce cases conducted in the Pekanbaru region in 2021. Extreme Learning Machine (ELM) is one of the artificial neural network technologies that can forecast. The benefit of this approach is that it has a low error rate and can train data thousands of times faster than typical feedforward algorithms. This study used the Extreme Learning Machine technique to forecast the number of divorces at Bangkinang city's religious court, where 108 divorces are expected to occur between January 2018 and December 2022. The number of neurons in the hidden layer is tested using MSE at random for hidden layer 1, 10, 50, 100, and 200 neurons. The Bangkinang religious court's divorce prediction with the lowest MSE is based on a data comparison of 80%: 20% and produces an up-and-down pattern for the number of divorces predicted for 2023: 164 in January, 66 in February, 72 in March, 74 in April, and 92 in May. If there is an increase in divorce in the upcoming month, the religious court in Kota Bangkinang can use the information that the Extreme Learning Machine can provide to come up with a solution.
Prediksi Jumlah Perceraian Menggunakan Metode Multilayer Perceptron Ikhsanul Hamdi; Elvia Budianita; Fadhilah Syafria; Iis Afrianty
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6291

Abstract

Divorce is a situation when a married couple decides to end their relationship and separate legally. The increasing number of cases in divorce cases filed at the Bangkinang Religious Court every month has led to a gradual increase and decrease. This study uses the Multilayer Perceptron (MLP) method and evaluates using Mean Squared Error (MSE) to determine prediction accuracy. The data used is divorce data from the Bangkinang Religious Court from January 2014 to December 2022 collected and processed from the Religious Court office. A total of 102 data in the form of time series data. In this study using MLP which consists of three layers, namely the input layer, hidden layer, and output layer. And using architectural testing consisting of 6-7-1, 6-9-1, and 6-12-1 with learning rate parameters: 0.01, 0.03, 0.09 with a comparison of training and test data 70:30, 80:20, 90 :10. Based on the test results using MSE, the best architecture was obtained, namely by comparing data 90:10 with 6-9-1 architecture, learning rate: 0.03, Epoch: 300, Alpha fixed value: 0.1, MSE results were successfully obtained: 0.01144 and the pattern of the number of splits from January until May 2023 has decreased, thus, this MLP can provide predictive results that help in predicting the number of divorces.
Analisis Sentimen Masyarakat Terhadap Kenaikan Biaya Haji Tahun 2023 Menggunakan Metode Naïve Bayes Classifier Hertati; Elin Haerani; Novriyanto; Fadhilah Syafria
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1457

Abstract

The Indonesian government through a meeting of the Ministry of Religion and Commission IVIII of the DPR-RI agreed on the cost of organizing the Hajj pilgrimage (BPIH) i1444 iH/2023 iM, an average of IDR 90,050,637.26 per irregular pilgrimage. However, this policy gave rise to various public responses. The public's anger regarding the increase in Hajj fees in 2023 was found on the social media iTwitter. In this study, we conducted a sentiment classification analysis of Tweets to determine public opinion regarding the increase in Hajj costs in 2023 using the naïve Bayes classifier method because this method tends to be simple and easy to use. The data set used was 3000 tweets with a total of 1866 positive data, 415 negative data. This research resulted in an accuracy value of 81.46% in the 70:30 data division, in the 80:20 data division, namely 80.74% and in the data division. 90:10 which is 79.04. In this research, there were more positive responses from the public, this proves that the increase in Hajj costs in 2023 can be accepted by the public. The highest accuracy in this study was 81.46% with a 70:30 data split. It is recommended that further research use other algorithms to see a comparison of the results of different algorithms in classifying public sentiment regarding the increase in the cost of Hajj in 2023.
Analisis Sentimen Tanggapan Masyarakat Terhadap Kenaikan Biaya Haji Tahun 2023 Menggunakan Metode K- Nearest Neighbor (KNN) Hafsyah; Elin Haerani; Novriyanto; Fadhilah Syafria
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1471

Abstract

The Indonesian government implemented a policy of increasing the cost of Hajj in 2023, but the policy has attracted many positive and negative comments among the public. Public comments are taken from the social media network Twitter, because it contains a lot of information so that it attracts the interest of most people. With the increase in Hajj costs in 2023, it is necessary to conduct sentiment analysis. This study uses  the K-Neearest Neighbor method  because it is easy to apply and the data used are divided into two classes, positive and negative. The results of research on the application of  the K-Nearest Neighbor method in  sentiment analysis of the increase in Hajj costs in 2023 using 3,000 data taken from Twitter comments. The tweet data  used, there were 1866 positive comments and 415 negative comments and the total net data of 2281, judging from the amount of positive data compared to negative  data, obtained an accuracy value of 81.17% in 70:30 data sharing, 79.87% in 80:20 data sharing, 77.73% in 90:10 data sharing. Meanwhile, the highest accuracy value was 81.17% with  82.48% precision, 97.67% recall, F1- Score 89.43%.  In this study, there were more positive responses, this proves that the increase in Hajj costs in 2023 using  the K-Nearest Neighbor (KNN)  method can be accepted by the community
Klasifikasi Data Penerimaan Zakat dengan Algoritma K-Nearest Neighbor Alfin Hernandes; Siska Kurnia Gusti; Fadhilah Syafria; Lestari Handayani; Siti Ramadhani
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1528

Abstract

National Amil Zakat Agency (BAZNAS) is an institution responsible for managing zakat established by the government. BAZNAS has a presence in every district or city, and one of them is the BAZNAS in the city of Pekanbaru. BAZNAS in Pekanbaru city is responsible for distributing zakat to various empowerment programs, one of which is the Pekanbaru Cares program. Currently, BAZNAS in Pekanbaru city is facing issues related to the method of distributing zakat, where the process of determining the criteria for zakat recipients is still being done manually by the committee of BAZNAS in the city of Pekanbaru. This condition is considered inefficient and poses one of the challenges that need to be addressed. To overcome the mentioned constraints, steps are needed to improve the effectiveness and efficiency of data collection for potential zakat recipients. One of the solutions is to implement a classification system to facilitate the data collection process, using the K-Nearest Neighbor (KNN) method. This approach functions as a tool to classify data for potential beneficiaries. This research aims to classify data and measure the accuracy in assessing the eligibility of zakat recipients based on predetermined criteria, utilizing the K-Nearest Neighbor (K-NN) algorithm. A total of 602 data from BAZNAS in the city of Pekanbaru were used in this study, by dividing the training and test data, namely divided 90:10, 80:20, and 70:30 splits. The evaluation results from the confusion matrix of k=3, k=5, k=7, k=9, and k=11 show that the highest accuracy is achieved at k=5 with an 80:20 split, with an accuracy rate of 89.3%. Furthermore, a precision of 87.3% and a recall of 91.4% can also be attained through this approach.
Penerapan Algoritma Naïve Bayes Classifier Dalam Klasifikasi Status Gizi Balita dengan Pengujian K-Fold Cross Validation Nurainun Nurainun; Elin Haerani; Fadhilah Syafria; Lola Oktavia
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3414

Abstract

Nutritional status is a condition related to nutrition that can be measured and is the result of a balance between nutritional needs in the body and nutritional intake from food. In Indonesia, there are still many nutritional problems such as malnutrition and other nutritional problems. This research will use the Naïve Bayes Classifier algorithm with K-Fold Cross Validation testing. The data used is data on the nutritional status of toddlers in August 2022 at the Rambah Samo I Health Center. Attributes in this study include Gender, Birth Weight, Birth Height, Age at Measurement, Weight, Height, ZS BB/U, BB/U, ZS TB/U, and TB/U. Determination of the nutritional status of toddlers in this study was based on the BB/TB index which consisted of 6 classes, namely severely wasted, wasted, normal, possible risk of overweight, overweight, and obese. From the research conducted, it was found that the Naïve Bayes Classifier algorithm with K-Fold Cross Validation can correctly classify the nutritional status of toddlers. From data processing using 10-Fold Cross Validation on the Naïve Bayes Classifier algorithm, it is known that the highest accuracy value is 82.94% in the 5th iteration, while the lowest accuracy value is 65.88% in 6th iteration. With an average overall accuracy value of 75.47%. Meanwhile, the average precision value obtained is 81.36% and the average recall value is 75.47%.
Clustering Vaksinasi Penyakit Mulut dan Kuku Menggunakan Algoritma Fuzzy C-Means Yusril Hidayat; Alwis Nazir; Reski Mei Candra; Suwanto Sanjaya; Fadhilah Syafria
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3416

Abstract

Foot and Mouth Disease is a disease that attacks cloven hooves, this disease spreads very quickly and the mortality rate of infected animals is up to 100%. FMD is caused by type A picornaviridae virus, namely Apthaee epizootecae, which has a development period of 1-14 days after the animal is infected. The delay in handling it can cause many livestock to die and have an impact on cattle farmers. One of the steps taken to prevent the spread of this disease is to eradicate all livestock. The Riau Provincial Government has taken steps to prevent vaccination of all livestock in Riau Province in the form of preventing this disease from becoming more widespread. From these problems, this research will form a data cluster for the PMK program in Riau Province so that the government can improve supervision of livestock to prevent re-outbreaks of foot and mouth disease in Riau Province. The method used is data mining with the Fuzzy C-means algorithm and the data used comes from the Department of Animal Husbandry and Animal Health in Riau Province. The best cluster results after testing is 2 clusters. The most numerous clusters are in cluster 1 with a total of 48704 cows and cluster 2 with a total of 21232. The validity test using the DBI gets a value of 0.416, so it is still far from good
Implementasi Algoritma K-Nearest Neighbor Untuk Menentukan Klasifikasi Kelulusan Mahasiswa Teknik Informatika Suswantia Andriani; Alwis Nazir; Reski Mai Candra; Fadhilah Syafria; Iis Afrianty
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.3914

Abstract

Kinerja sebuah universitas dapat dinilai semakin baik jika terdapat peningkatan jumlah mahasiswa yang berhasil menyelesaikan masa studinya tepat waktu. Perguruan tinggi harus meningkatkan kualitas akademik mahasiswa dalam proses perkuliahan untuk hasil yang optimal. Namun kenyataannya banyak keterlambatan kelulusan mahasiswa pada Universitas Islam Negri Sultan Syaif Kasim Riau terkhusus jurusan Teknik Informatika ini yang menjadi permasalahan. Oleh karena itu dibutuhkannya sebuah analisa mengklasifikasi data kelulusan mahasiswa dengan memanfaatkan proses data mining. Penelitian ini menerapkan algoritma K-Nearest Neighbor (K-NN) pada proses klasifikasi. Tujuan penelitian ini adalah hasil dari klasifikasi kelulusan ini diharapkan dapat memberikan kontribusi bagi pihak fakultas teknik dan universitas dalam melakukan evaluasi dan perbaikan terhadap sistem pembelajaran, sehingga menghasilkan lulusan tepat waktu dan berkualitas. Data yang digunakan pada penelitian ini sebanyak 613 data 5 tahun terakhir dari tahun 2016 hingga 2020 dengan pembagian data dengan rasio 80 data untuk pelatihan (training) dan 20 data untuk pengujian (testing). Hasil evaluasi confusion matrix dari K = 3, K = 5, K = 7 menghasilkan akurasi tertinggi diperoleh ketika K = 3 dengan akurasi 93,06%, presisi 99,09%, dan recall 99,58%. Dari hasil penelitian, dapat ditarik kesimpulan bahwa penerapan data mining berhasil menciptakan model klasifikasi dengan memanfaatkan algoritma K-Nearest Neighbor (K-NN) dalam mengklasifikasikan status kelulusan mahasiswa pada program sudi Teknik Informatika di Universitas Islam Negeri Sultan Syarif Kasim Riau
Perbandingan Jarak Metrik pada Klasifikasi Jamur Beracun Menggunakan Algoritma K-Nearest Neighbor (K-NN) Andre Suarisman; Alwis Nazir; Fadhilah Syafria; Liza Afriyanti
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4511

Abstract

Mushrooms are organisms from the kingdom fungi that have a fleshy body structure and can be consumed, but there are some species of mushrooms that are not safe to eat and have specific characteristics, so distinguishing between edible and poisonous mushrooms can be tricky due to the almost identical appearance of various mushroom species. Errors in identifying edible mushrooms can impact the health of consumers who consume the mushrooms. Evaluating the performance of various methods on a dataset is a key step in determining the most suitable classification method. This research is about how to measure the performance of classification methods on toxic mushroom datasets using the K-Nearest Neighbor algorithm with several metrics such as euclidean, manhattan and minkowski, which is a method for classifying new data based on proximity to existing training data. The results obtained in this study with several distance metrics can be concluded that the accuracy value of the manhattan metric is better than the euclidean and minkowski metrics. Because the manhattan metric gets the highest accuracy result of 99% with K = 100 and the lowest 82% with K = 3000, while the euclidean metric gets accuracy results with a value of 98% with K = 100 and 72% with K = 3000, and the minkowski metric gets accuracy results with a value of 96% at K = 100 and 64% at K = 3000.
Co-Authors Abdul Aziz Abdullah, Said Noor Abdussalam Al Masykur Adrian Maulana Adzhima, Fauzan Afriyanti, Liza Agung Syaiful Rahman Agus Buono Agustina, Auliyah Ahmad Paisal Aji Pangestu Adek Akbar, Lionita Asa Alfin Hernandes Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Amalia Hanifah Artya Aminuyati Andre Suarisman Aprima, Muhammad Dzaky Ariq At-Thariq Putra Baehaqi Bib Paruhum Silalahi Boni Iqbal Che Hussin, Ab Razak Darmila Dede Fadillah Deny Ardianto Devi Julisca Sari Dina Septiawati Dodi Efendi Eka Pandu Cynthia Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Hearani Ellin Haerani Elvia Budianita Faska, Ridho Mahardika Fatma Hayati Fauzan Adzim Febi Nur Salisah Febi Yanto Felian Nabila Fitra Lestari Fitri Insani Fitri Insani Fitri Wulandari Fratiwi Rahayu Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Guswanti, Widya Habibi Al Rasyid Harpizon Hafez Almirza Hafsyah Hara Novina Putri Harni, Yulia Hertati Ibnu Afdhal Ihda Syurfi Iis Afrianty Iis Afrianty Ikhsan, Tomi Ikhsanul Hamdi Indrizal, Habibi Putra Inggih Permana Irma Sanela Ismail Marzuki Ismar Puadi Isnan Mellian Ramadhan Israldi, Tino Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril Karina Julita Khair, Nada Tsawaabul Lestari Handayani Lestari Handayani Lili Rahmawati Liza Afriyanti Lola Oktavia Lola Oktavia M Fikry M. Afif Rizky A. Ma'rifah, Laila Alfi Masaugi, Fathan Fanrita Maulana Junihardi Mawadda Warohma Mazdavilaya, T Kaisyarendika Mhd. Kadarman Mori Hovipah Mori Hovipah Morina Lisa Pura Muhammad Affandes Muhammad Alvin Muhammad Fahri Muhammad Fikry Muhammad Hanif Abdurrohman Muhammad Ichsanul Bukhari Muhammad Irsyad Muhammad Syafriandi, Muhammad Muhammad Taufiq Muhammad Yusril Haffandi Muhammad Yusuf Fadhillah Mulyono, Makmur Muslimin, Al’hadiid Nabyl Alfahrez Ramadhan Amril Nailatul Fadhilah Nazir, Alwis Nazruddin Safaat H Negara, Benny Sukma Neni Sari Putri Juana Nesdi Evrilyan Rozanda Nining Nur Habibah Novriyanto Novriyanto Nurainun Nurainun Okfalisa Okfalisa Permata, Rizkiya Indah Pizaini Pizaini Puspa Melani Almahmuda Putra, Fiqhri Mulianda Putri Mardatillah Putri, Widya Maulida Rahmad Abdillah Rahmad Abdillah Rahmad Kurniawan Rahmadhani, R. Raja Sultan Firsky Ramadhan, Aweldri Ramadhan, Muhammad Ilham Ramadhani, Siti Reski Mai Candra Reski Mai Candra Reski Mai Candra Reski Mei Candra Riska Yuliana Roni Salambue Said Nanda Saputra Satria Bumartaduri Silfia Silfia Siti Ramadhani Siti Sri Rahayu Suswantia Andriani Suwanto Sanjaya Syaputra, Muhammad Dwiky Teddie Darmizal Vitriani, Yelvi Wulandari, Fitri Yaskur Bearly Fernandes Yusra, Yusra Yusril Hidayat Zabihullah, Fayat Zulastri, Zulastri