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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Pengabdian Kepada Masyarakat (Mediteg) Jurnal Humaniora Teknologi Jurnal Humaniora Teknologi Brilliance: Research of Artificial Intelligence Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Teknomatika: Jurnal Informatika dan Komputer
Permadi, Jaka
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Religion Aerospace Engineering Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Transportation Other

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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

Abstract

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.
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

Abstract

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.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (649.995 KB) | DOI: 10.25126/jtiik.2018551113

Abstract

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.
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

Abstract

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. 
Sistem Informasi Daily Report pada Kantor COP-CPP di PT Arutmin Indonesia Tambang AsamAsam Berbasis Web Maharani, Novyta; Permadi, Jaka
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 9 No. 2 (2025): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/xjmt2t38

Abstract

Proses pelaporan harian di kantor COP-CPP PT Arutmin Indonesia Tambang AsamAsam masih dilakukan secara manual menggunakan file Excel dengan banyak tabel, sehingga menyulitkan pencarian dan penggabungan data. Penelitian ini bertujuan mengembangkan sistem informasi daily report berbasis web untuk meningkatkan efisiensi dan akurasi pengelolaan data. Metode pengembangan menggunakan model waterfall, dengan tahapan analisis, desain, implementasi, dan pengujian menggunakan black box. Sistem dikembangkan menggunakan PHP Framework Codeigniter 4 dan database MySQL. Hasil pengujian menunjukkan seluruh fitur sistem berfungsi dengan baik, mulai dari upload, pencarian, pengelolaan, hingga download data laporan harian. Sistem ini terbukti dapat mempercepat proses pelaporan, meminimalkan kesalahan input, dan mendukung pengambilan keputusan yang lebih cepat dan akurat di lingkungan kerja PT Arutmin Indonesia
Co-Authors Amelia, Noor Hendrik Setyo Utomo Herfia Rhomadhona Kusrini, Wiwik Maharani, Novyta Muhammad Alfian, Muhammad Noor Amelia Rifani, Much Safar, Safar Sholeha, Eka Wahyu Winda Aprianti