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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Information Management For Educators And Professionals (IMBI) JURNAL ILMIAH MATRIK Sebatik METIK JURNAL Tematik : Jurnal Teknologi Informasi Komunikasi TEPIAN Informatika Wicida
Pitrasacha Adytia
STMIK Widya Cipta Dharma
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Spatial Coordinate Trial : Converting Non-Spatial Data Dimension for DBSCAN Arriyanti, Eka; Arfyanti, Ita; Adytia, Pitrasacha
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1962

Abstract

In big data, noise in data mining is a necessity. Its existence depends on data and algorithm, but it does not mean the algorithm caused noise. Although the advantages of the Density Based Spatial Clustering Application with Noise, DBSCAN algorithm, in executing spatial data (two-dimensional data) have been widely discussed, but it has not been convincing in executing non-spatial data. As an algorithm should perform well on any data for optimizing data mining, this research proposes a trial to convert dimensions of non-spatial data into 2 dimensions for executing with DBSCAN algorithm, and a different input value for epsilon to know about its minimum which begins arising noise in the execution. Method of analysis in trial is with considering the attributes of non-spatial data as variables that represent coordinate points, rather than cardinality. Technically, it is assumed that 2-dimensional coordinate axes as a spot point for coordinate with more than or equal 3 dimensions according to development of Cartesian coordinate system, by first paying attention to relationship of variables (attributes). This way is then called Spatial Coordinate. The different input values are with paying attention to numbers from non-zero minimum distance to the forth of epsilon where the epsilon is in integer. The results of trial and testing on clusters formed, with Silhouette Coefficient, point out that the clusters are well, strong, and quality enough. Therefore, this research gives a new way on how preprocessing non-spatial data for DBSCAN algorithm performance.
PENERAPAN METODE EPQ(ECONOMIC PRODUCTION QUANTITY) PADA PENGENDALIAN BAHAN BAKU LAUNDRY DI SAMARINDA LAUNDRY MART BARBASIS ANDROID Ekawati, Hanifah; Adytia, Pitrasacha; Yunita, Yunita
Jurnal Ilmiah Matrik Vol 22 No 1 (2020): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (990.928 KB) | DOI: 10.33557/jurnalmatrik.v22i1.840

Abstract

Samarinda Laundry Mart is a business that provides laundry services, besides that it is also a supplier of laundry raw materials in Samarinda. Control of laundry raw materials in the laundry business is a complicated problem. Therefore one method that can be used for raw material control is the EPQ method (Economic Production Quantity) where the EPQ method can determine the optimal production level, optimal production frequency, optimal time cycle to minimize total inventory costs. The results of this study are made inventory control applications using the EPQ method that can make calculations automatically. Only by entering monthly data that is already available. Users can also make transactions using the application so that all data has been integrated in the database to facilitate management in the laundry business. In addition, this application can also print transaction reports and turnover reports.
Desain dan Implementasi Job Matching System Menggunakan Metode Kombinasi WSM-TOPSIS Pitrasacha; Eka Arriyanti
METIK JURNAL Vol 4 No 2 (2020): METIK Jurnal
Publisher : LP3M Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v4i2.180

Abstract

Penelitian ini dilakukan untuk mendesain dan mengimplementasikan sebuah job matching system sebagai fasilitator antara lulusan perguruan tinggi yang mencari kerja dan penyedia kerja. Sistem berkerja dengan melakukan pencocokan antara kebutuhan dan spesifikasi tenaga kerja dari penyedia kerja dengan profil kompetensi lulusan perguruan tinggi. Studi kasus penelitian dilakukan di STMIK WICIDA. Metode kombinasi antara weighted scoring model (WSM) dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) digunakan untuk meningkatkan akurasi hasil pencocokan. Target khusus dari dari penelitian ini adalah membuat job matching system yaitu sistem rekomendasi bagi pencari kerja dan penyedia kerja. Pencari kerja akan mendapatkan rekomendasi lowongan pekerjaan yang cocok dengan skill yang dimiliki, sedangkan bagi penyedia kerja akan mendapatkan rekomendasi calon tenaga kerja yang sesuai dengan spesifikasi yang dibutuhkan
Desain dan Implementasi Job Matching System Menggunakan Metode Kombinasi WSM-TOPSIS Pitrasacha; Eka Arriyanti
METIK JURNAL Vol 4 No 2 (2020): METIK Jurnal
Publisher : LP3M Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v4i2.180

Abstract

Penelitian ini dilakukan untuk mendesain dan mengimplementasikan sebuah job matching system sebagai fasilitator antara lulusan perguruan tinggi yang mencari kerja dan penyedia kerja. Sistem berkerja dengan melakukan pencocokan antara kebutuhan dan spesifikasi tenaga kerja dari penyedia kerja dengan profil kompetensi lulusan perguruan tinggi. Studi kasus penelitian dilakukan di STMIK WICIDA. Metode kombinasi antara weighted scoring model (WSM) dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) digunakan untuk meningkatkan akurasi hasil pencocokan. Target khusus dari dari penelitian ini adalah membuat job matching system yaitu sistem rekomendasi bagi pencari kerja dan penyedia kerja. Pencari kerja akan mendapatkan rekomendasi lowongan pekerjaan yang cocok dengan skill yang dimiliki, sedangkan bagi penyedia kerja akan mendapatkan rekomendasi calon tenaga kerja yang sesuai dengan spesifikasi yang dibutuhkan
Spatial Coordinate Trial : Converting Non-Spatial Data Dimension for DBSCAN Eka Arriyanti; Ita Arfyanti; Pitrasacha Adytia
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1962

Abstract

In big data, noise in data mining is a necessity. Its existence depends on data and algorithm, but it does not mean the algorithm caused noise. Although the advantages of the Density Based Spatial Clustering Application with Noise, DBSCAN algorithm, in executing spatial data (two-dimensional data) have been widely discussed, but it has not been convincing in executing non-spatial data. As an algorithm should perform well on any data for optimizing data mining, this research proposes a trial to convert dimensions of non-spatial data into 2 dimensions for executing with DBSCAN algorithm, and a different input value for epsilon to know about its minimum which begins arising noise in the execution. Method of analysis in trial is with considering the attributes of non-spatial data as variables that represent coordinate points, rather than cardinality. Technically, it is assumed that 2-dimensional coordinate axes as a spot point for coordinate with more than or equal 3 dimensions according to development of Cartesian coordinate system, by first paying attention to relationship of variables (attributes). This way is then called Spatial Coordinate. The different input values are with paying attention to numbers from non-zero minimum distance to the forth of epsilon where the epsilon is in integer. The results of trial and testing on clusters formed, with Silhouette Coefficient, point out that the clusters are well, strong, and quality enough. Therefore, this research gives a new way on how preprocessing non-spatial data for DBSCAN algorithm performance.
Algoritma Apriori Untuk Rekomendasi Produk Pada Website Penjualan UD Rahmat Becled Asep Nurhuda; Pitrasacha Adytia; Rahmat Hidayat
INFORMATION MANAGEMENT FOR EDUCATORS AND PROFESSIONALS : Journal of Information Management Vol 4 No 1 (2019): INFORMATION MANAGEMENT FOR EDUCATORS AND PROFESSIONALS (Desember 2019)
Publisher : Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (480.781 KB)

Abstract

Abstrak: Rekomendasi produk pada web penjualan UD Rahmat Becled bertujuan untuk membantu pemilik usaha dalam menampilkan produk-produknya kepada pelanggan agar menarik minat pelanggan sehingga membeli produk tersebut. Tentu saja dalam menarik minat pelanggan, produk yang ditampilkan harus sesuai dengan aktifitas pelanggan selama berikteraksi pada website penjualan, Sehingga dibutuhkan algoritma yang dapat membantu proses rekomendasi tersebut dan salah satu algoritma yang dapat digunakan yaitu apriori. Sistem rekomendasi menggunakan algoritma apriori pada web penjualan UD Rahmat Becled dikembangkan menggunakan metode pengembangan sistem waterfall, alat bantu pengembangan menggunakan sitemap, Flowchart, dan Entity Relationship Diagram. Hasil rekomendasi produk menggunakan algoritma apriori bermanfaat bagi website penjualan UD Rahmat Becled. Kata kunci: Algoritma Apriori, Rekomendasi Produk, Web Penjualan. Abstract: Product recommendations on the UD Rahmat Becled e-commerce aim to assist business owners in displaying their products to customers in order to attract customers' interest so they buy the product. Of course, in attracting customer interest, the products displayed must be in accordance with customer activities during the interaction on the system, so we need an algorithm that can help the recommendation process and one of the algorithms that can be used is a priori. The a priori algorithm based recommendation system on the UD Rahmat Becled e-commerce was developed using the waterfall system development method, development aids using a sitemap, Flowchart, and Entity Relationship Diagram. The product recommendation results using a priori algorithm are useful for the UD Rahmat Becled e-commerce. Keywords: Apriori Algorithm, E-Commerce, Product Recommendations.
PERANCANGAN DAN IMPLEMENTASI MOVEMENT SLIDER KAMERA GUNA MENUNJANG TEKNIK SINEMATOGRAFI DAN FOTOGRAFI MENGGUNAKAN ARDUINO NANO Didi Kuswandi; Tommy Bustomi; Pitrasacha Adytia
Jurnal Informatika Wicida Vol 10 No 2 (2021): Juli 2021
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (383.753 KB) | DOI: 10.46984/inf-wcd.1824

Abstract

Penelitian ini memuat bagaimana membangun Perancangan dan Implementasi Movement Slider Kamera Guna Menunjang Teknik Sinematografi dan Fotografi Menggunakan Arduino Nano adalah suatu penelitian yang bertujuan untuk membantu kebutuhan manusia di bidang multimedia khususnya foto dan video, fotografer dan pembuat videographer saat ini membutuhkan peralatan canggih dan dapat membantu mempermudah pengambilan gambar.Salah satu manfaat dari penggerak atau slider kamera jarak jauh ini ialah berfungsi untuk menggerakan kamera menggunakan android tanpa harus dikendalikan secara manual dengan di pegang. Berdasarkan hal tersebut akan dibuat kendali gerak pada slider kamera menggunakan kendali Android berbasis mikrokontroler.Pengujian dilakukan menggunakan White Box, Black Box. Dengan membangun sebuah alat dengan rancangan dari beberapa komponen, yaitu: Arduino Nano, Bluetooth HC-05, IC Driver Motor A4988, dan Motor Nema. Hasil dari penelitian ini berupa pergerakan kamera secara gerakan panning left / right, tilt up / down, crab left / right berdasarkan dari inputan yang dikirim dari smartphone android dengan menekan salah satu tombol button yang tersedia di aplikasi.Pembuatanaplikasicontrolmenggunakan App Blynk,untuk pengontrolan slider kamera menggunakan bluetooth HC-05 sebagai penghubung akses kendali antara aplikasi di smartphone dengan mikrokontroler.
PENCARIAN DRIVER DRY CLEAN TERDEKAT DENGAN METODE HAVERSINE FORMULA Ekawati Yulsilviana; Pitrasacha Adytia; I Nengah Riandika
Sebatik Vol 25 No 1 (2021): Juni 2021
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (879.005 KB) | DOI: 10.46984/sebatik.v25i1.1210

Abstract

Sistem pemesanan dry cleaning konvensional tidak bisa menemukan driver terdekat dengan pemesan. Penelitian ini bertujuan untuk membangun sistem untuk membantu pencarian driver terdekat dengan pemesan agar cepat dalam mengambil pakaian kotor dan sebaliknya mencari driver terdekat dari dry cleaning jika pakaian sudah selesai. Penelitian ini menerapkan metode haversine formula untuk pencarian driver terdekat, Google Maps sebagai pembangun peta digital, dan dikembangkan berbasis mobile. Sistem perancangan pada penelitian ini menggunakan Unified Modeling Language (UML) yang terdiri dari Use Case Diagram, Activity Diagram, Class Diagram, Sequence Diagram, dan Deployment Diagram. Pengujian sistem dilakukan dengan menggunakan white box testing dan beta testing.
Application of the Finite State Machine Method in the Desktop-Based “Heroes Of Dawn” RPG Turn-Based Game Muhammad Fachri Sanjaya; Heny Pratiwi; Pitrasacha Adytia
TEPIAN Vol 2 No 2 (2021): June 2021
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v2i2.348

Abstract

FSM (Finite State Machine) is a method of implementing artificial intelligence that is applied to make a decision on NPC (Non Player Character). The application of FSM that is often encountered is to form an NPC with intelligence, so that the NPC can respond to the player's character so that the NPC seems to be able to think. Games have various types (genres) and are increasingly varied in line with the development of hardware and software technology. Writing will focus on games with the Role Playing Game genre or often called RPG. Games in general use Artifical Intelligence in their systems to make the game more interesting to play. Artifical Intelligence is usually applied to NPC (Non Player Character) / Enemy in the game or opponents who must be defeated, one of the applications of Artifical Intelligence in the game to be used in this research is the Finite State Machine (FSM) method.
Perbandingan Algoritma Machine Learning Dalam Mendeteksi Serangan DDOS Wahyuni; Pitrasacha Adytia
Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) Vol 9 No 2 (2022): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2022
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v9i2.1070

Abstract

Ddos is an attack method by sending a lot of packets into a network that causes the device not to run according to its function. This attack will result in machine or network resources cannot be accessed or used by the user. Various methods are used to detect DDOS attacks on SDN [4] , namely statistical methods, machine learning, SDN architecture, blockchain, Network Function Virtualization, honeynets, network slicing, and moving target defense. Because so many people use machine learning to detect DDoS attacks, it is necessary to do further research to find out which one is the best and has high accuracy. Therefore, a research entitled “Comparison of Machine Learning Algorithms in Detecting DDoS Attacks was made. In this study, three machine learning algorithms will be compared, namely XGBoost, Decision Tree and ANN. The methods used are data acquisition, data understanding, data preparation, modeling, performance evaluation, and conclusions. In this study it can be said that for accuracy, the highest model is XGBoost in determining attacks, but to execute it requires the longest time among other models tested. While Decision tree also has high accuracy, slightly below XGBoost, but the time required to execute is fast or short. Therefore, in this study it can be said that the Decision Tree is the best model in detecting and classifying DDoS attacks.Keywords: Ddos Attack, Machine Learning, Decision Tree, XGBoost, ANN.
Co-Authors Arriyanti, Eka Didi Kuswandi Ekawati Yulsilviana Ekawati, Hanifah Heny Pratiwi I Nengah Riandika Ita Arfyanti Muhammad Fachri Sanjaya Nurhuda, Asep Rahmat Hidayat Tommy Bustomi Ujang Sumarwan Wahyuni - Wahyuni Yunita Yunita