https://ejournal.unsrat.ac.id/v3/index.php/IJIDS/issue/feedIndonesian Journal of Intelligence Data Science2024-05-17T11:24:21+08:00Winsy Wekuijids@unsrat.ac.idOpen Journal Systems<p>The <strong>Indonesian Journal of Intelligence Data Science</strong> is a scientific journal that accepts and disseminates research results to advance science by publishing them twice a year in May and November. <span class="Y2IQFc" lang="en">The Jurnal IJIDS already has <a href="https://issn.lipi.go.id/terbit/detail/1517672631" target="_blank" rel="noopener"><strong>E-ISSN: 2988-0416.</strong></a></span></p> <p><span class="Y2IQFc" lang="en"><strong><span class="type">DOI: </span><span class="id"><a href="https://doi.org/10.35799/ijids.v2i1">https://doi.org/10.35799/ijids.v2i1</a></span></strong></span></p> <p>This journal accepts scientific writings that are based on the theme of Data Science, with the scope of research covering the use or development of several methods such as statistical learning (relationships between variables to gain insight) and machine learning (models capable of making predictions) which are applied to various data from sampling results structured or unstructured which arise from various problems in the field of scientific research such as the environment, etc.</p> <p><strong>Indonesian Journal of Intelligence Data Science</strong> (IJIDS) is a peer-reviewed open-access journal for original research articles, review articles and technical reports related to all aspects of data science and its application in the field of business, geography, engineering, social management, etc. IJIDS was launched in 2022 and published quarterly by Mathematics Department, Sam Ratulangi University.<br /><br />Current areas of interest include, but are not limited to:</p> <ul> <li>Machine learning, Deep Learning and intelligent management</li> <li>Data Mining and Business Analytics</li> <li>Data statistics and decision making</li> <li>Intelligent computing and algorithms</li> <li>Cloud Computing</li> <li>Spatial Data Science</li> <li>Business Intelligence and Data Science</li> <li>Big Data Analysis </li> <li>Data Security Management</li> <li>Data visualization</li> <li>Data Science Architecture</li> <li>Data Science Technolgy</li> <li>Data Warehouse and OLAP</li> <li>Natural Language Processing</li> <li>Forecasting, Prediction and Interpolation</li> </ul>https://ejournal.unsrat.ac.id/v3/index.php/IJIDS/article/view/50153Pemetaan Ekosistem Mangrove Menggunakan Unsupervised Learning dengan Data Remote Sensing2023-07-29T08:57:14+08:00Yesica Simangunsongyesicastivany@gmail.comWinsy Christo Deilan Wekuwinsy_weku@unsrat.ac.idMarline Sofiana Paendongmarlinepaendong@unsrat.ac.id<p><em>The Mangrove Ecosystem is one of the coastal ecosystems that experiences a lot of threats from various activities. The latest land use and land cover information is very necessary in regional development planning and environmental monitoring. One way to obtain this information is through remote sensing satellite image data processing. Therefore, this research aims to identify changes in mangrove forests using Machine Learning algorithms, namely K-Means and Random Forest, as well as determine the ability of the K-Means and Random Forest algorithms in identifying these land changes. The results of land cover changes in the mangrove ecosystem of Palaes Village in 2013 and 2021 based on Unsupervised Learning using the K-Means and Random Forest algorithms were clustered into 4 and 5 land cover classes based on different color classes, namely mangrove, sea water, other plants, raised sand and soil . Selection of the number of clusters is very important to get accurate results. The cluster results show that the clustering of the Palaes Village mangrove ecosystem looks more accurate on the 5 cluster K-Means map.</em></p>2024-01-15T00:00:00+08:00Copyright (c) 2024 Yesica Simangunsong, Winsy Christo Deilan Weku, Marline Sofiana Paendonghttps://ejournal.unsrat.ac.id/v3/index.php/IJIDS/article/view/50154PENERAPAN SISTEM PENDUKUNG KEPUTUSAN REKOMENDASI SEKOLAH MENENGAH PERTAMA MENGGUNAKAN METODE SIMPLE ADDITVE WEIGHTING DI KABUPATEN MINAHASA SELATAN2023-07-29T10:10:11+08:00Claudio Dandi J Paisadandp.claudio@gmail.comNelson Nainggolann-nelson@unsrat.ac.idWinsy Christo Deilan Wekuwinsy_weku@unsrat.ac.id<p>The development of internet technology today is growing rapidly. <br />The internet makes it easier for humans to access information and <br />do various things, we can find the information we need and expand <br />communication networks with technological sophistication, as well <br />as information on junior high schools in South Minahasa district <br />which is still less effective. The purpose of this study was to create <br />a website-based decision support system for junior high schools in <br />South Minahasa district as a means of information for parents and <br />students. The method used in designing this system is the waterfall <br />method. The resulting program is a decision support system for <br />junior high schools in South Minahasa district.</p>2024-05-15T00:00:00+08:00Copyright (c) 2024 Claudio Dandi J Paisahttps://ejournal.unsrat.ac.id/v3/index.php/IJIDS/article/view/55581Peramalan Indeks Harga Konsumen Di Kota Palu Menggunakan Metode Arima (Autoregressive Integrated Moving Average) Dalam Model Intervensi Fungsi Step2024-05-17T11:24:21+08:00Ilka Soldarima Landailkasoldarima@gmail.comDjoni Hatidjadhatidja@unsrat.ac.idYohanes A.R Langiyohaneslangi@unsrat.ac.id<p>Analisis intervensi adalah sebuah pendekatan dalam analisis runtun waktu yang digunakan untuk memahami dampak beberapa peristiwa yang menyebabkan perubahan pola data pada suatu titik waktu t. Penelitian ini bertujuan untuk melakukan prediksi terhadap Indeks Harga Konsumen di kota Palu dari bulan Januari hingga Juni 2023 menggunakan metode ARIMA dengan fungsi step. Data yang digunakan dalam penelitian ini adalah Indeks Harga Konsumen di Kota Palu dari Januari 2015 hingga Desember 2022. Pada bulan Januari 2020, terjadi sebuah peristiwa yang signifikan atau intervensi yang berlangsung dalam jangka waktu yang cukup lama. Oleh karena itu, model intervensi yang diduga menggunakan fungsi step dengan orde b=0, s=36, dan r=0. Setelah melakukan analisis terhadap model ARIMA (1,2,2), didapatkan model intervensi yang kemudian digunakan untuk melakukan prediksi terhadap IHK kota Palu selama 6 bulan ke depan. Hasil prediksi yang diperoleh adalah 116.23, 116.27, 116.23, 116.18, 116.13, dan 116.08.</p> <p><strong>Kata kunci:</strong> Analisis Intervensi, Kota Palu,Peramalan.</p>2024-05-15T00:00:00+08:00Copyright (c) 2024 Djoni Hatidjahttps://ejournal.unsrat.ac.id/v3/index.php/IJIDS/article/view/50112Geographic Information System (GIS) for Mapping Flood-prone Using Weighting and Scoring Method (Case Study: Tikala District)2023-07-27T19:16:49+08:00Chandra Haryonochandraharyono59@gmail.comJullia Titaleyjuly.titaley@gmail.comWinsy Christo Deilan Wekuwinsy_weku@unsrat.ac.idChristian Alderi Jeffta Soewoehchristian.suwuh@unsrat.ac.id<p>Peristiwa bencana banjir yang terjadi akibat meningkatnya aliran air sungai yang menggenangi wilayah dataran. Penelitian ini bertujuan untuk mengetahui daerah di Kecamatan Tikala Kota Manado yang memiliki dampak terjadinya banjir dengan pemanfaatan penginderaan jarak jauh agar memberikan kesadaran lebih bagi perintahan dan masyarakat sekitar. Metode yang digunakan dalam penelitian ini menggunakan pembobotan dan scoring yaitu pemberian nilai bobot pada tiap parameter dan pemberian skor pada tiap kelas parameter sesuai dengan klasifikasinya, kemudian dilakukan tahap overlay peta dengan menggunakan aplikasi QuantumGIS (QGIS). Hasil yang diperoleh yaitu dalam bentuk file peta yang telah menunjukan kelas kerawanan banjir di tiap kelurahan di Kecamatan Tikala Kota Manado dengan tingkatan kerawanan rendah hinggah tinggi. Tingkat kerawanan di kelurahan Banjer dan Tikala Ares ada pada kategor Rawan dan Sangat Rawan dengan total nilai 56.350 – 63.600.</p>2024-05-15T00:00:00+08:00Copyright (c) 2024 Chandra Haryonohttps://ejournal.unsrat.ac.id/v3/index.php/IJIDS/article/view/50114Determination of Shortest Path for Flood Evacuation in Manado City Using Dijkstra's Algorithm.2023-07-27T19:43:23+08:00Lusyane Humunelusyane.humune@gmail.comJullia Titaleyjuly.titaley@gmail.comMahardika Inra Takaendenganmahardika@unsrat.ac.id<p>One of the common potential disasters in Manado City is floods. Manado City has a history of destructive floods that damage infrastructure and cause loss of life, resulting in significant losses. Lack of awareness among the public about evacuation locations is also due to the absence of the shortest path for flood evacuation. Therefore, a determination of the shortest path for flood evacuation is made using Dijkstra's Algorithm, which is used to search for graphs in solving the shortest distance problem using distance and time weights from 3 evacuation points and 1 shelter point. Three shortest evacuation routes are found using the Dijkstra algorithm.</p>2024-05-15T00:00:00+08:00Copyright (c) 2024 Lusyane Humunehttps://ejournal.unsrat.ac.id/v3/index.php/IJIDS/article/view/50119SISTEM PELAYANAN AKADEMIK PENGAJUAN SURAT MAHASISWA FMIPA UNSRAT BERBASIS WEB2023-07-28T01:11:56+08:00Richard Pongantungrichardpongantung106@student.unsrat.ac.idWinsy Christo Deilan Wekuwinsy_weku@unsrat.ac.idWisard Widsli Kalengkonganwisard.kalengkongan@unsrat.ac.idChristian Alderi Jeffta Soewoehchristian.suwuh@unsrat.ac.id<p>Sistem Pelayanan Akademik pada Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) Universitas Sam Ratulangi (UNSRAT) merupakan elemen kunci dalam mendukung efisiensi administrasi dan pengelolaan data mahasiswa. Namun, proses manual yang digunakan dalam pengajuan surat oleh mahasiswa menyebabkan adanya hambatan dalam pelayanan dan memperpanjang waktu pemrosesan. Untuk mengatasi permasalahan tersebut, penelitian ini bertujuan untuk mengembangkan Sistem Pelayanan Akademik Pengajuan Surat Mahasiswa FMIPA UNSRAT Berbasis Web. Penelitian ini menggunakan metode pengembangan sistem <em>SDLC Waterfall</em> dan juga menggunakan metode pemodelan visual berbasis objek yang digunakan dalam perancangan sistem berorientasi objek yaitu <em>UML</em> dengan mengumpulkan kebutuhan sistem melalui wawancara dan observasi langsung di lingkungan FMIPA UNSRAT. Selanjutnya, desain sistem dilakukan dengan mengidentifikasi fitur-fitur utama, struktur database, dan antarmuka pengguna yang memudahkan mahasiswa dalam mengajukan surat. Hasil dari penelitian ini adalah sebuah sistem berbasis web yang menyediakan platform untuk pengajuan surat mahasiswa secara online. Sistem ini memungkinkan mahasiswa untuk mengisi formulir pengajuan surat, mengunggah dokumen pendukung, dan memantau status permohonan secara real-time.</p>2024-05-15T00:00:00+08:00Copyright (c) 2024 Richard Pongantunghttps://ejournal.unsrat.ac.id/v3/index.php/IJIDS/article/view/50142Web-Based Pharmacy Information System Using Personal Extreme Programming (PXP) Method with MVC Architecture2023-07-28T15:14:33+08:00cindy Lahamalahamacindy@gmail.comChriestie Ellyanne Juliet Clara Montolaluchriestelly@unsrat.ac.idEdwin Tendaedwin.phill@icloud.com<p><em>A web-based drug store data framework is outlined to encourage the productive administration and deals of solutions in a drug store with consistent integration. To realize this objective, the Individual Extraordinary Programming (PXP) strategy has been received to oversee the development of this framework. Within the setting of this venture, the PXP strategy is custom fitted to a littler advancement environment. The drug store data framework is outlined with the Model-View-Controller (MVC) engineering, which isolates commerce rationale, introduction, and client interaction into unmistakable components. This points to improve adaptability, versatility, and generally framework practicality. The results of this extend are anticipated to provide a web-based drug store data framework open through a user-friendly interface, proficient in pharmaceutical stock administration, and supporting exact installment frameworks. As a result, it is expected that operational proficiency and client benefit will altogether progress.</em></p>2024-05-15T00:00:00+08:00Copyright (c) 2024 cindy Lahama, Chriestie Ellyanne Juliet Clara Montolalu, Edwin Tenda