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Geofencing Lokasi Rawan Pencurian Sepeda Motor Di Kota Pontianak Berbasis Android Arfiyan, Fandika Roinaldi; Insani, Rachmat Wahid Saleh; Sucipto, Sucipto
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 13, No 2: Agustus 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v13i2.1967

Abstract

Crime, including theft, is a social issue that raises concerns and disrupts community peace, especially in the city of Pontianak. Among various problems, the importance of environmental security systems is emphasized in efforts to enhance order, safety, and combat crime, with the hope of reducing criminal activity so that community activities can proceed smoothly. This research has resulted in an Android application aimed at increasing users' awareness of motorcycle theft in Pontianak through the use of geofencing technology. The application utilizes geofencing features to identify and monitor high-risk areas, providing alerts when entering or leaving these zones. Firebase serves as the backend to store theft-prone data. Location permissions are required, and inter-component communication within the application provides additional information. Based on black box testing and questionnaires conducted, it can be concluded that the developed application is well-received by users, functions effectively, and helps identify routes vulnerable to motorcycle theft in Pontianak.Top of FormKeywords: Android; Motorcycle Theft; Security; Location System AbstrakKriminalitas, termasuk pencurian, merupakan isu sosial yang menimbulkan kekhawatiran dan mengganggu kedamaian masyarakat, terutama di kota Pontianak. Di antara berbagai permasalahan, pentingnya sistem keamanan lingkungan ditekankan untuk meningkatkan ketertiban, keamanan, dan mengatasi kejahatan, dengan tujuan menurunkan tingkat kejahatan guna lancarnya aktivitas masyarakat. Penelitian ini menghasilkan sebuah aplikasi Android yang dirancang untuk meningkatkan kesadaran pengguna terhadap pencurian sepeda motor di Pontianak melalui teknologi geofencing. Aplikasi ini menggunakan fitur geofencing untuk mengidentifikasi dan memonitor area-area berisiko tinggi, memberikan peringatan saat masuk atau keluar dari zona-zona tersebut. Firebase digunakan sebagai backend untuk menyimpan data mengenai daerah rawan pencurian. Izin lokasi diperlukan, dan komunikasi antar komponen dalam aplikasi menyediakan informasi tambahan. Berdasarkan pengujian black box dan kuesioner yang dilakukan, dapat disimpulkan bahwa aplikasi yang dikembangkan diterima dengan baik oleh pengguna, berfungsi dengan efektif, dan membantu mengidentifikasi rute-rute yang rentan terhadap pencurian sepeda motor di Pontianak. 
KLASIFIKASI OBESITAS DENGAN ALGORITMA C5.0 BERDASARKAN POLA MAKAN DAN KONDISI FISIK Nasim, Muhammad; Siregar, Alda Cendekia; Insani, Rachmat Wahid Saleh
Jurnal Khatulistiwa Informatika Vol 12, No 2 (2024): Periode Desember 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jki.v12i2.23974

Abstract

Obesitas merupakan salah satu masalah kesehatan global yang terus meningkat, dipengaruhi oleh berbagai faktor seperti gaya hidup yang tidak sehat, pola makan tinggi kalori, dan kurangnya aktivitas fisik. kondisi ini dapat menyebabkan berbagai komplikasi serius seperti penyakit jantung, diabetes tipe 2, tekanan darah tinggi, dan berbagai kondisi kesehatan lainnya yang mengurangi kualitas hidup dan meningkatkan angka kematian. dalam penelitian ini, kami mengembangkan sistem klasifikasi tingkat obesitas menggunakan algoritma c5.0, yang dikenal karena kemampuannya dalam menangani data yang kompleks dan multikategori. algoritma ini juga efektif dalam menghasilkan model pohon keputusan yang mudah diinterpretasi oleh tenaga kesehatan. dataset yang digunakan dalam penelitian ini terdiri dari 2.111 sampel dengan 17 variabel, termasuk jenis kelamin, usia, tinggi badan, berat badan, kebiasaan makan, riwayat keluarga, dan aktivitas fisik. model c5.0 yang dibangun menunjukkan hasil yang sangat baik dengan akurasi mencapai 94,78% pada data uji. evaluasi model dilakukan menggunakan matriks kebingungan yang menunjukkan performa tinggi dengan nilai akurasi, presisi, recall, dan f1-score yang konsisten di hampir semua kategori obesitas. secara khusus, model ini mencapai nilai sempurna dalam mendeteksi kategori obesity type iii, yang menunjukkan kemampuannya yang kuat dalam mengidentifikasi tingkat obesitas yang paling parah. hasil ini menunjukkan bahwa algoritma c5.0 dapat menjadi alat yang efektif untuk mendukung sistem pendukung keputusan dalam mendeteksi risiko obesitas, yang pada akhirnya dapat membantu dalam pengembangan strategi pencegahan dan intervensi yang lebih efektif untuk meningkatkan kesehatan masyarakat.
Foto 360 dalam Sistem Informasi Geografis Pemetaan Lokasi Wisata di Android Rachmat Wahid Saleh Insani
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 5 No 1 (2024)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.5.1.219

Abstract

Pariwisata merupakan peringkat ketiga sektor yang menyumbangkan devisa terbesar untuk Indonesia. Penerapan teknologi merupakan salah satu upaya pemerintah untuk meningkatkan sektor pariwisata. Inovasi teknologi dalam bentuk program komputer yang diterapkan pada sektor pariwisata akan mengubah pola bisnis pariwisata menjadi responsif, kreatif, dan inovatif. Penelitian ini akan membangun Sistem Informasi Geografis dengan memanfaatkan format foto 360 untuk menampilkan lokasi obyek wisata. Sistem memanfaatkan Google Maps API untuk menampilkan peta digital dan rute menuju obyek wisata. Foto obyek wisata ditangkap menggunakan kamera DSLR dan dijahit untuk menjadi foto 360 menggunakan aplikasi PTGui. Foto 360 ditampilkan dalam bentuk equirectangular panorama di aplikasi Android menggunakan WebView dengan Panellum. Lokasi obyek wisata diperoleh dari teknik geotagging, yakni mengakses matadata lokasi dari dalam EXIF tag yang ada di file foto.
Implementasi Aplikasi Web untuk Basis Data Disabilitas di National Paralympic Committee of Indonesia Kalimantan Barat: Implementation of Web Application for Database on National Paralympic Committee of Indonesia Kalimantan Barat Insani, Rachmat Wahid Saleh; Alamsyah, Dedi; Sucipto, Sucipto
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 10 No. 5 (2025): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v10i5.8648

Abstract

The National Paralympic Committee of Indonesia (NPCI) in West Kalimantan is an organization tasked with supporting athletes with disabilities. It supervises training initiatives, manages competition registrations, improves welfare, cultivates character, and ensures the health standards of athletes with disabilities. This agency oversees the data of athletes with disabilities, presently documented on handwritten records maintained in filing cabinets. This community service initiative develops a web application for NPCI West Kalimantan to facilitate the management of data about athletes with impairments. The application has been deployed using hosting services, guaranteeing that the application files are hosted on a web server and own a domain name. This web application has facilitated NPCI West Kalimantan in accessing, adding, modifying, and deleting data about athletes with impairments.
Implementasi Information Retrieval System Aksesibilitas Buku di Perpustakaan dengan Metode Dice Similarity Vika Ummu Hani; Syarifah Putri Agustini Alkadri; Rachmat Wahid Saleh Insani
JURNAL RISET RUMPUN ILMU TEKNIK Vol. 4 No. 1 (2025): April : Jurnal Riset Rumpun Ilmu Teknik
Publisher : Pusat riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurritek.v4i1.4561

Abstract

The problem of accessibility and efficiency of book search is still a major challenge in school libraries, especially those that still rely on manual systems. This study aims to implement and test the effectiveness of an information retrieval system (IRS) based on the Dice Similarity method in the SMA Negeri 1 Siantan Library, West Kalimantan. The research method used is experimental descriptive quantitative, with data collection through observation, interviews, and documentation, as well as system testing on collection data and library user activities. The system was developed using the Laravel framework and MySQL database, and evaluated using precision and recall metrics. The results showed that the system has a Precision value of 100% and a variable Recall value, with the highest value of 66.66% and the lowest of 14.28%. The implementation of this system significantly speeds up the search process, minimizes recording errors, and increases user satisfaction. The findings recommend the adoption of Dice Similarity-based IRS as an applicable solution for school libraries in supporting literacy and easy access to information. The findings are expected to be a reference for the development of library information systems in educational environments with limited resources.
Klasifikasi Pemilihan Bibit Unggul Kelapa Sawit Menggunakan Algoritma Naïve Bayes Elsa Damayanti; Barry Ceasar Octariadi; Rachmat Wahid Saleh Insani
JURNAL RISET RUMPUN ILMU TEKNIK Vol. 4 No. 1 (2025): April : Jurnal Riset Rumpun Ilmu Teknik
Publisher : Pusat riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurritek.v4i1.4991

Abstract

Oil palm is a key commodity supporting Indonesia’s economy through exports and employment. The industry’s success depends heavily on the selection of superior seedlings, which determine productivity, crop quality, and resistance to pests and diseases. Manual selection, however, often leads to subjectivity and inconsistency due to limited human resources and genetic variation. To address this, the study applies the Naïve Bayes algorithm for classifying oil palm seedlings based on seven variables: height, stem diameter, number of leaves, leaf color, disease resistance, root growth, and fruit yield. Using an explanatory quantitative method, the study follows seven stages: identifying problems, literature review, collecting 1,000 data entries from PT Intitama Berlian Perkebunan, data pre-processing, system modeling (UML), algorithm implementation, and evaluation using a confusion matrix and black box testing. Data was split into 80% training and 20% testing. The Naïve Bayes-based classification achieved 95% accuracy and perfect recall (1.00) for the superior seedling class. However, its performance on the minority class (non-superior seedlings) was weaker due to dataset imbalance. Black box testing verified all system functions worked correctly, enabling effective and efficient use by administrators. The study concludes that Naïve Bayes improves objectivity, efficiency, and accuracy in seedling selection. Nonetheless, attention is needed on data balancing and optimization to maintain consistent performance across classes. This system shows strong potential as a decision-support tool in plantations and promotes digital transformation in agricultural processes.
Sistem Pendukung Keputusan Rekomendasi Penerima Bantuan Iuran BPJS Kesehatan Menggunakan Metode ROC dan SMART Masroni; Syarifah Putri Agustini Alkadri; Rachmat Wahid Saleh Insani
JURNAL FASILKOM Vol. 13 No. 3 (2023): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v13i3.6271

Abstract

Bantuan Sosial Penerima Bantuan Iuran (PBI) yang merupakan program dari pemerintah untuk pelayanan kesehatan dalam pemberian bantuan yang berupa jaminan kesehatan kepada masyarakat indonesia, dalam proses rekomendasi untuk Penerima Bantuan Iuran (PBI) BPJS Kesehatan di Dinas Sosial Kabupaten Sambas selama ini masih berdasarkan hasil penilaian musyawarah desa (MUSDES) baru kemudian diserahkan ke Dinas Sosial, penilaian dan pendataan yang dilakukan masih secara manual yaitu pendataan dengan mengisi form kertas verifikasi dan validasi data yang belum terkomputerisasi dengan baik, sehingga memakan waktu paling cepat seminggu atau 21 hari paling lama. Sistem yang dibangun menggunakan metode ROC dan SMART bertujuan agar dapat membantu pihak pendata untuk mengurangi waktu dalam pendataan dan efektif dalam merekomendasikan penerima bantuan sosial PBI sesuai kriteria yang digunakan. Hasil penelitian ini hanya 13 alternatif yang akan direkomendasikan untuk mendapatkan bantuan sosial yaitu alternatif bahar dengan nilai 0,07381 sampai alternatif tambrin dengan nilai 0,6993 dengan hasil perhitungan yang cukup akurat diketahui melalui pengujian MAPE (Mean Absolute Perentage Error) menunjukkan hasil 25,31%.
E-Government Media Informasi Alat Kelengkapan Dewan Provinsi Bali dan Media Diskusi Berbasis Website Sugiartawan, Putu; Rustina, I Dewa Ketut Rai; Saleh Insani, Rachmat Wahid
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 1 No 2 (2018): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (945.848 KB) | DOI: 10.33173/jsikti.17

Abstract

The development of technology and information at this time is very fast, one of which is the internet. The ease of digging information through a website encourages state institutions to use the website as a medium of information to the public. The purpose of this research is to build and design E-Gov media information in the DPRD of the Province of Bali which is used as a medium of information and discussion that can be utilized by the community and the council. Website content only displays information about the board such as board fittings with the following sub-sections; Leadership of the Board, Commissions, Bamus, Banggar, Baleg, Honorary Board. As well as allowing website visitors to be able to express aspirations to the board
Implementasi Data Mining untuk Klasifikasi Penyakit Stroke Menggunakan Algoritma K-Nearest Neighbor Enkan Feny Nopitasari; Syarifah Putri Agustini Alkadri; Rachmat Wahid Saleh Insani
KREATIF: Jurnal Pengabdian Masyarakat Nusantara Vol. 5 No. 3 (2025): Jurnal Pengabdian Masyarakat Nusantara
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/kreatif.v5i3.8215

Abstract

Stroke remains a major global health challenge, with diagnoses often delayed, particularly in primary care facilities with limited infrastructure. This study aimed to develop a stroke risk classification system using the K-Nearest Neighbor (KNN) algorithm, optimized through comprehensive data preprocessing. A secondary dataset of 5,110 patient records was processed using mean imputation for missing BMI values, winsorization to manage outliers, label encoding for categorical variables, and Min-Max normalization for feature scaling. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied prior to stratified data splitting into 70% training and 30% testing sets. The KNN model with K=5 demonstrated strong performance, achieving 96% accuracy, 96% precision, 99% recall, and a 97% F1-score on the test data. Multivariate correlation analysis identified age, hypertension, and blood glucose levels as the primary predictors of stroke risk, consistent with established clinical pathophysiology. These findings highlight the critical role of cardiometabolic risk factors in early detection. The system was implemented as a web application using Streamlit, enabling rapid and interactive screening in primary healthcare centers with minimal infrastructure. This practical application has the potential to assist healthcare providers in early stroke detection, accelerating clinical intervention and reducing the likelihood of long-term complications. Nevertheless, several limitations exist. The reliance on secondary data introduces the possibility of regional bias, and the use of SMOTE generates synthetic data that may affect model generalizability. Future research is recommended to validate the model across multi-source datasets, apply advanced hyperparameter tuning, and explore ensemble learning techniques to further enhance predictive reliability. In conclusion, the KNN-based classification system demonstrates promising potential as a practical decision-support tool for early stroke risk assessment in resource-limited healthcare settings.