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Implementation of KNN and AHP-TOPSIS as Recommendation System for Mustahik Selection Aprianti, Winda; Permadi, Jaka; Rhomadhona, Herfia; Amelia, Noor
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3883

The National Amil Zakat Agency (BAZNAS) has the task of managing zakat on a national scale, including zakat. The number of prospective zakat recipients is greater than the availability of zakat funds distributed, which has an impact on the need for a selection process for mustahik. In this research, to assist the mustahik selection process, KNN will be used to classify mustahik candidates who meet the requirements, AHP to obtain consistent weights, and TOPSIS to provide recommendations for the order of mustahik whose zakat will be distributed. The dataset used in the research consisted of 77 data consisting of the criteria for number of dependents, husband's job, wife's job, total income, total expenses, and acceptance status of mustahik candidates. The application of KNN produced 15 data that were declared worthy of being considered mustahik. In the next stage, using AHP, the weights for each criterion were obtained at 12.66%, 9.23%, 10.10%, 45.96% and 22.04%. These weights were used in the TOPSIS decision support system and the results obtained were that the 76th mustahik candidate was the first ranked candidate to be proposed as a mustahik. In this research, a system was also built using KNN and AHP-TOPSIS using the PHP programming language as a recommendation system tool.

Pembangunan Aplikasi Asesmen Kompetensi Untuk Meningkatkan Kinerja LSP Sabella, Billy; Fathurrahmani, Fathurrahmani; Aprianti, Winda; Achmad, Ferdiyansyah; Tina, Ridha Rahma
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6054

Professional Certification Institute (PCI) of Politeknik Negeri Tanah Laut has the task of carrying out competency assessments for prospective graduates. Competency assessment management starting from registration, the certification process, and reporting certification results is still done manually, so it requires a long management time and is prone to errors. Ineffective management of the 2 existing schemes will further impact the decline in the performance of the PCI of Politeknik Negeri Tanah Laut which is currently in the process of increasing the scope of 16 schemes. This is the rationale for the need for an application that can help manage competency certification data so as to improve Professional Certification Institute performance. The method used in this research starts from the stages of needs identification, design, implementation, testing and evaluation, implementation and training, and system development. The research that has been carried out provides the results of the PCI of Politeknik Negeri Tanah Laut information system which was built and has features for managing assessment data, assessor data, certification scheduling, competency test material components, certification reporting and activity documentation. The system built has been tested using black box testing and shows 100% successful feature functionality. System evaluation using User Acceptance Testing on 12 respondents showed acceptance results of 91.50%. Based on the test results, the assessment application that has been built helps improve Professional Certification Institute performance.

Implementation of K-Means Clustering for Social Assistance Recipients with Silhouette Score Evaluation Rhomadhona, Herfia; Kusrini, Wiwik; Aprianti, Winda; Permadi, Jaka
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.5900

The distribution of direct social assistance continues to face several challenges, particularly regarding inaccurate targeting and unequal allocation. One of the main causes of this issue is the lack of transparency in the distribution process, where assistance is often granted to individuals with familial ties to local committee members or even government officials. As a result, the groups most in need frequently do not receive the aid they deserve. This condition is also evident in Tanjung Village, Bajuin Subdistrict, Tanah Laut Regency. The manual process of grouping prospective aid recipients contributes to inaccuracies in targeting, which in turn leads to public dissatisfaction. To address this issue, this study applies the K-Means Clustering method to group potential social assistance recipients using data from 150 individuals and three main attributes: age, occupation, and income. The method clusters the data based on the similarity of characteristics, thus supporting a more equitable and efficient identification process. The evaluation is conducted using the Silhouette Coefficient to assess the quality of clustering. The results indicate that the highest Silhouette Score is achieved at k=2k = 2k=2, with a value of 0.8278, suggesting that dividing the data into two clusters provides the most optimal configuration. The Silhouette Score tends to decrease as the number of clusters increases, confirming that adding more clusters does not necessarily improve the quality of separation.

Rancang Bangun Sistem Informasi Rawa BaTIK Berbasis Web Rifani, Much; Permadi, Jaka; Aprianti, Winda
Jurnal Humaniora Teknologi Vol. 6 No. 1 (2020): Jurnal Humaniora Teknologi
Publisher : P3M Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jht.v6i1.76

Sistem pendaftaran Rawa BaTIK (Ruang Kegiatan Warga Belajar Aplikasi dan TIK) yang dikelola oleh Dinas Komunikasi dan Informatika Kabupaten Tanah Bumbu saat ini masih dilakukan dengan cara manual atau langsung ke kantor Dinas Kominikasi dan Informatika. Kantor Dinas Komunikasi dan Informatika yang jaraknya cukup jauh dari Pusat Kota Tanah Bumbu hal ini tentu saja memberatkan bagi Pelajar atau Masyarakat yang ingin mendaftar. Berdasarkan permasalahan tersebut, diperlukan suatu sistem yang dapat memfasilitasi para calon peserta untuk mendaftar secara online agar tidak perlu lagi datang ke kantor Dinas Komunikasi dan Informatika, maka dibuatlah Sistem Informasi Rawa BaTIK (Ruang Masyarakat Belajar Aplikasi dan TIK) Berbasis Web. Pembuatan sistem ini menggunakan metode waterfall dimana pembangunan sistem informasi menggunakan bahasa pemrograman PHP yang kemudian diuji menggunakan black box. Sistem informasi Rawa BaTIK telah berhasil dibangun dan hasil pengujian juga menunjukkan fungsionalitas sistem dapat berfungsi sesuai dengan hasil yang diharapkan.

The Comparison of Decision Tree and K-Nearest Neighbor Performance for Determining Mustahik Amelia, Noor; Aprianti, Winda
Brilliance: Research of Artificial Intelligence Vol. 3 No. 2 (2023): Brilliance: Research of Artificial Intelligence, Article Research November 2023
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v3i2.2953

The problem of poverty is one of the fundamental issues of concern to the Indonesian government. One of the methods used by Islam to alleviate poverty is through zakat from Badan Amil Zakat Nasional (BAZNAS). Currently, the distribution of zakat is divided into two, namely in the form of consumptive zakat and productive zakat. Productive zakat is aimed at people who need business capital. To assist zakat managers in managing their funds, a mechanism is needed that can process mustahik data so that it can be selected more quickly and precisely using data mining. In this research, the data mining methods that will be used are K-nearest neighbor (KNN) and Decision Tree. The dataset used in this research is data obtained from BAZNAS and has been preprocessed to obtain a dataset with 7 attributes and 144 records. Decision trees, KNN Manhattan, and KNN Euclidean are used to predict mustahik candidates who are worthy of receiving zakat. The performance of the third method was tested using AUC and confusion matrix namely Accuracy, Precision, Recall, and F1 in each dataset split scenario of 70%:30%, 75%:25%, and 80%:20%. Based on the number of false positive and false negative results, the best performance obtained is KNN Euclidean with a dataset division scenario of 80%:20%.

Implementation of KNN and AHP-TOPSIS as Recommendation System for Mustahik Selection Aprianti, Winda; Permadi, Jaka; Rhomadhona, Herfia; Amelia, Noor
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3883

The National Amil Zakat Agency (BAZNAS) has the task of managing zakat on a national scale, including zakat. The number of prospective zakat recipients is greater than the availability of zakat funds distributed, which has an impact on the need for a selection process for mustahik. In this research, to assist the mustahik selection process, KNN will be used to classify mustahik candidates who meet the requirements, AHP to obtain consistent weights, and TOPSIS to provide recommendations for the order of mustahik whose zakat will be distributed. The dataset used in the research consisted of 77 data consisting of the criteria for number of dependents, husband's job, wife's job, total income, total expenses, and acceptance status of mustahik candidates. The application of KNN produced 15 data that were declared worthy of being considered mustahik. In the next stage, using AHP, the weights for each criterion were obtained at 12.66%, 9.23%, 10.10%, 45.96% and 22.04%. These weights were used in the TOPSIS decision support system and the results obtained were that the 76th mustahik candidate was the first ranked candidate to be proposed as a mustahik. In this research, a system was also built using KNN and AHP-TOPSIS using the PHP programming language as a recommendation system tool.

Implementasi Association Rules dengan Algoritma Apriori pada Dataset Kemiskinan Winda Aprianti; Khairul Anwar Hafizd; M. Redhy Rizani
Limits: Journal of Mathematics and Its Applications Vol. 14 No. 2 (2017): Limits: Journal of Mathematics and Its Applications Volume 14 Nomor 2 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Usaha pengentasan kemiskinan terus dilakukan di Kabupaten Tanah Laut. Untuk membantu pemerintah dalam perumusan kebijakan pengentasan kemiskinan maka diperlukan pengetahuan mengenai indikator yang berkaitan dengan kemiskinan dan bagaimana indikator-indikator tersebut saling mempengaruhi. Tujuan penelitian ini adalah menerapkan Association rules dengan algoritma Apriori pada dataset kemiskinan untuk mencari pola hubungan antar indikator. Dataset yang terdiri dari 46 atribut merupakan data sekunder BPS Kabupaten Tanah Laut dan BPS Provinsi Kalimantan Selatan tahun 2010-2014. Hasil penerapan association rules dengan algoritma apriori menggunakan minimum support 30% dan minimum confidence 80% menghasilkan 4614 rules hubungan antar indikator.

K-Means Clustering untuk Data Kecelakaan Lalu Lintas Jalan Raya di Kecamatan Pelaihari Aprianti, Winda; Permadi, Jaka
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 5: Oktober 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Kecelakaan lalu lintas di jalan raya masih menjadi penyumbang tingginya angka kematian di Indonesia, sehingga menjadi perhatian khusus bagi kepolisian di negara ini. Termasuk Kepolisian Resor (Polres) Tanah Laut, yang telah membuktikan perhatian tersebut dengan membentuk komunitas korban kecelakaan lalu lintas dan Pelatihan Pertolongan Pertama Gawat Darurat (PPGD). Tahapan awal pencegahan kecelakaan lalu lintas adalah dengan mengetahui faktor-faktor penyebab kecelakaan lalu lintas yang diperoleh melalui analisa data kecelakaan. Analisa tersebut dapat dilakukan dengan data mining, yaitu K-Means Clustering. K-Means Clustering mengelompokkan data menjadi beberapa cluster sesuai karakteristik data tersebut. Data kecelakaan lalu lintas dibagi menjadi 2 dataset, yakni dataset 1 dan dataset 2. Hasil cluster penerapan K-means clustering terhadap dataset 1 dan dataset 2 kemudian dilakukan pengujian silhoutte coefficient untuk mencari hasil cluster dengan kualitas terbaik. Pengujian silhoutte coefficient secara berurutan menghasilkan distance measure paling optimal yakni clustering dengan 4 cluster untuk dataset 1 dan clustering dengan 2 cluster untuk dataset 2. Selain memperoleh cluster dengan kualitas terbaik, penganalisaan data juga menghasilkan beberapa informasi kecelakaan lalu lintas yang sering terjadi, yakni faktor penyebab dan korban kecelakaan adalah pengemudi, umur korban adalah 9 sampai 28 tahun, dan keadaan korban kecelakaan adalah luka ringan. AbstractTraffic accidents on the highway are still contribute to the high mortality rate in Indonesia, which are becoming a special concern for the police. Including the Police of Tanah Laut Resort where prove themselves by established The Community of Traffic Accident Victims and Emergency First Aid Training. The first prevention of traffic accidents is knowing the factors causing traffic accidents which is obtained through the analysis of traffic accident’s data. It can be done through data mining, i.e. K-Means Clustering, which is clustering data into clusters according to characteristics of the data. Traffic accident data is divided into two datasets, namely dataset 1 and dataset 2. After obtaining the cluster results, the next step is to calculate silhoutte coefficient which is used to find the best quality cluster result. The result of testing silhoutte coefficient are clustering with 4 clusters for dataset 1 and clustering with 2 clusters for dataset 2. Analyzing data in this research also produces some information on traffic accidents that often occur, namely the causes and victims of accidents are drivers, the age of the victims is between 9 and 28 years old, and the circumstance of the accidents victims are minor injuries.

Implementasi Pelatihan Keterampilan Menjahit Busana Wanita Terhadap Taraf Ekonomi Peserta Pelatihan di LPPK Maherka Kabupaten Lombok Timur Aprianti, Winda; Herlina, Herlina
Transformasi : Jurnal Penelitian dan Pengembangan Pendidikan Non Formal Informal Vol. 10 No. 2 (2024): September
Publisher : Program Studi Pendidikan Luar Sekolah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jtni.v10i2.12991

Abstract: The problem raised in this research is to determine the implementation of women's clothing sewing skills training on the economic level of training participants at LPPK Mahherka, East Lombok Regency. This research aims to determine the implementation of women's clothing sewing skills training on the economic level of training participants at LPPK Mahherka, East Lombok Regency. This research uses an evaluative method with the CIPP evaluation model (Context, Input, Process, Product). The sample was determined using a saturated sample with a total of 20 participants. The data collection techniques used were questionnaires, observation and documentation. The data analysis method uses statistical analysis methods with a Likert scale percentage formula with answer score formulas, ideal scores and percentages. The results of the percentage analysis using the Likert scale percentage statistical approach show that the components context in the good category, namely 60.86%, component input in the quite good category, namely 62.11%, component process in the quite good category, namely 63.22%, component product is in the good category, namely 60.5%, while the overall results are in the quite good category, namely 61.54%, which means it has run optimally. So it can be concluded that the implementation of women's clothing sewing skills training on the economic level of training participants at LPPK Mahherka, East Lombok Regency has been running effectively in accordance with the objectives achieved.Keywords :Â Training, Women's Clothing Sewing Skills, Economic LevelAbstrak: Masalah yang di angkat dalam penelitian ini adalah untuk mengetahui implementasi pelatihan keterampilan menjahit busana wanita terhadap taraf ekonomi peserta pelatihan di LPPK Mahherka Kabupaten Lombok Timur. Penelitian ini bertujuan untuk mengetahui implementasi pelatihan keterampilan menjahit busana Wanita terhadap taraf ekonomi peserta pelatihan di LPPK Mahherka Kabupaten Lombok Timur. Penelitian ini menggunakan metode evaluatif dengan model evaluasi CIPP (Context, Input, Process, Product). Penentuan sampelnya menggunakan sampel jenuh dengan jumlah subjek 20 peserta. Adapun teknik pengumpulan data yang digunakan yaitu angket, observasi dan dokumentasi. Metode analisis data menggunakan metode analisis statistik dengan rumus persentase skala likert dengan rumus skor jawaban, skor ideal dan presentase. Hasil dari analisis persentase dengan pendekatan statistik persentase skala likert ini menunjukkan bahwa komponen context masuk kategori baik yakni 60,86%, komponen input masuk kategori cukup baik yakni 62,11%, komponen process masuk kategori cukup baik yakni 63,22%, komponen product masuk kategori baik yakni 60,5% sedangkan hasil dari keseluruhan yakni berada dalam kategori cukup baik yakni 61,54% yang berarti sudah berjalan secara optimal. Jadi dapat disimpulkan bahwa implementasi pelatihan keterampilan menjahit busana wanita terhadap taraf ekonomi peserta pelatihan di LPPK Mahherka Kabupaten Lombok Timur sudah berjalan secara efektif sesuai dengan tujuan yang dicapai.Kata Kunci:Â Pelatihan, Keterampilan Menjahit Busana Wanita, Taraf Ekonomi

Prediction Active Case of Covid-19 with ERNN Aprianti, Winda; Permadi, Jaka; Rhomadhona, Herfia
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 1 (2022): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i1.4874

SARS-CoV-2 is known as Covid-19 has been spread in all world since end of 2019. Indonesia, including South Kalimantan has detected first Covid-19 in March 2020. This pandemic has affected in all entirely live in Indonesia. This makes Covid-19 be the main focus of the government. The government has provided aid and imposed restrictions on activities. These policies require planning that can be a solution. Careful planning requires an overview of the data on active cases that are positive for Covid-19. This overview can be obtained through prediction. In this research, Elman Recurrent Neural Network (ERNN) was used to predict active cases of Covid-19. Architecture of ERNN was used ERNN with 3 input nodes, 2 hidden nodes, and 2 context nodes. The data used is 277 data, which is then divided into training data and testing data, respectively 90%-10%, 80%-20%, and 70%-30%. ERNN with a learning rate of 0.1 until 0.9 is applied to data on active cases of Covid-19, then Mean Absolute Percentage Error (MAPE) is calculated to find out performance of model generated by ERNN. The results showed that all of MAPE were below 10% with the smallest MAPE as 3.21% for scenario 90:10 and learning rate 0.6. MAPE value which is less than 10% indicates that ERNN has very good predictive ability.

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