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All Journal CSRID (Computer Science Research and Its Development Journal) Informasi Interaktif
Kusrini, Kusrini
Universitas AMIKOM Yogyakarta
Computer Science & IT

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IMPLEMENTASI DETEKSI TEPI MENGGUNAKAN METODE QUADRANT TREE CLASSIFIER PADA PEMISAHAN OBJEK BERBASIS DIGITAL IMAGE PROCESSING (STUDI KASUS : OBJEK BENDERA NEGARA) Nurcahyo, Azriel Christian; Wati, Vera; Profesi, Dwinda Etika; Kusrini, Kusrini
Informasi Interaktif Vol 4, No 3 (2019): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

Edge detection is segmentation of image input that aims to determine the edge by marking the detail part of an image. From some previous studies it has not been shown the results of detection to be able to separate objects from the center of the image input image itself.The purpose of this study is to perform an edge detection function by dividing into nodes using the concept of the Quadran Tree Classifier method to be applied to the case study of the object colored image of the using national flag. Some input images have different levels of complexity and pixels, including the Korean flag, Wales flag, and the flying Indonesian flag.The method is the adoption of  tree data structure, where each has 4 nodes the same number of child nodes. If the node has children, number of nodes must be 4, recursively doing the loop. The working concept of this method split and merge segmentation. The results of object segmentation combined in accordance with the homogeneity of colors, especially those with confusion.This research show which is able to observe the scanning pixel on image Korean flag and the flying Indonesian flag, however level pixel 520 x 347 such as Wales flag, this method is unable to separate between line object that is not nudge. The pixel resolution has an effect with total time execution segmentation (minute/sec), total segmentation identified and the total colour. Keywords : edge detection, quadrant tree, digital image processing, image, segmentation
EVALUASI USER INTERFACE PADA APLIKASI E-COMMERCE (STUDI KASUS INFORMA DAN IKEA) Ilkham, Sundari; Kusrini, Kusrini; Arief, M. Rudyanto
Informasi Interaktif Vol 4, No 3 (2019): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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ABSTRACT Increasingly complex human needs cause changes in the community environment. This is indicated by the increasingly rapid development of information technology, especially information technology related to online shopping applications through e-commerce applications. The development of technology that is currently being talked about and is the subject of discussion is to develop technology towards modern business by using mobile-based e-commerce applications. Anywhere and anytime quickly and in real time, people can easily make purchases of any product related to the needs of the house they are looking for. The results of this study were to determine the magnitude of the heuristic value of the quality of the mobile e-commerce application user interface Informa and Ikea according to users using the Nielsen 10 principle method. It is expected that the results of this study can be used as input for e-commerce mobile application developers Informa and Ikea to be better, and can be proposed to improve the user interface for e-commerce applications Informa and Ikea. Keywords:  E-Commerce, User Interface, 10 Nielsen Principles. 
Sistem Pemilihan Ruang Rawat Inap Menggunakan Metode Weighted Product dan K-Nearest Neighbor Adiatma, Biva Candra Lutfi; Muahidin, Zumratul; Kusrini, Kusrini
CSRID (Computer Science Research and Its Development Journal) Vol 13, No 1 (2021): CSRID FEBRUARI 2021
Publisher : Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid.13.1.2021.01-11

Abstract

Rawat Inap merupakan istilah yang berarti proses perawatan pasien oleh tenaga kesehatan profesional akibat penyakit tertentu, dimana pasien diinapkan di suatu ruangan dirumah sakit. Ruang instalasi rawat inap merupakan ruang tempat pasien dirawat. Ruangan rawat inap dulunya hanya berupa bangsal yang dihuni oleh banyak orang sekaligus. Penelitian ini fokus pada pengembangan sistem pendukung keputusan untuk memprioritaskan pasien covid-19 atas ketersediaan ruang rawat inap. Sistem ini dirancang untuk membantu pihak rumah sakit dalam memberikan kenyamanan bagi para pasien yang sedang dirawat. Metode yang digunakan dalam penelitian ini adalah K-Nearest Neighbor untuk mendapatkan klasifikasi dari gambar rontgen pasien kemudian akan dikonversikan ke dalam bentuk bobot sesuai kriteria yang akan diolah berdasarkan kriteria – kriteria yang telah ditentukan oleh rumah sakit kemudian dilanjutkan menggunakan metode Weighted Product untuk perhitungan bobot kepentingan dan bobot pangkat setelah itu akan didapatkan nilai vektor s dan yang terakhir akan didapatkan nilai vektor v dari setiap alternatif untuk mendapatkan prioritas atas ketersediaan ruang rawat inap.. Hasil penelitian menunjukkan bahwa sistem dapat membantu pihak rumah sakit dalam memprioritaskan pasien covid-19 atas ketersediaan ruang rawat inap.
TEXT MINING DOKUMEN TWEET PADA TWITTER UNTUK KLASIFIKASI KARAKTER CALON KARYAWAN Saifudin, Saifudin; Kusrini, Kusrini; Fatta, Hanif Al
Informasi Interaktif Vol 5, No 1 (2020): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

Recruitment is a means to prepare as many workers as possible according to the requirements and qualifications expected by the organization. In recruitment one of the things that is calculated is the character of the prospective employee itself. Companies or organizations usually carry out psychological tests and interviews to get the character of prospective employees. This will make the recruitment process longer and require a lot of money. One way to find out a person's character can be done by looking at the publication of daily activities on various social media. In this study the classification of prospective employees is based on tweets found on twitter. The results of this study are grouping prospective employees based on their characters using the naïve bayes classifier algorithm. From the research that has been done naïve bayes classifier algorithm has an accuracy accuracy of an average of 52% by weighting using the term document frequency.Keywords: Naïve Bayes Classifier, TFIDF, Character
CLUSTERING DATA NILAI ADAPTIF SISWA MENGGUNAKAN ALGORITMA K-MEANS Khoironi, Khoironi; Kusrini, Kusrini; Arief, Rudyanto
Informasi Interaktif Vol 5, No 2 (2020): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

Student success rates and low student failure rates are a reflection of the quality of education, at this time the value does not determine the success of students in the next stage, but the uniqueness in itself that is represented in each grade they achieve, maybe students fail in mathematics but he succeeded in chemistry. it does not indicate he failed but he has shown its strengths in other respects namely other subjects, therefore this study seeks to find the positive side of students by classifying the value of subjects achieved by implementing the K-Means method in its application which will provide cluster output of each subject. K-Means method was chosen because this method can group items in k groups (where k is the number of groups or clusters desired, so the results of this termination are clusters of student grades grouped by subjects. K-Means is effective for clustering data by showing good accuracy value, this is indicated by the results of evaluations using Bouldin index davies on student data using K-Means which is equal to -1,478.   Keywords:  Cluster, K-Means, Education, Davies-Bouldin. 
PENERAPAN ALGORITMA PALGUNADI PADA SPLIT DELIVERY VEHICLE ROUTING PROBLEM UNTUK PENDISTRIBUSIAN MULTI PRODUK Wulandari, Sri; Kusrini, Kusrini; Arief, M. Rudyanto
Informasi Interaktif Vol 4, No 1 (2019): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

                     Transportation is one area in the supply chain management that determines how and when to send goods to consumers. Determination of distribution routes has an effect on transportation costs. In delivering goods, the company must be able to determine the configuration of the distribution line appropriately so that shipping becomes fast and does not cost much. This study, we will develop a SDVRP solution method for multiple-products distribution. Solving the SDVRP problem for multiple-products distribution will use the Palgunadi Algorithm with the aim of determining the distribution route so that the minimum total distribution costs are obtained. This study uses data about the distribution of fuel in the NTT region with many agents 8 and many types of products 3. There are 3 types of tankers used. The results of the study produced 3 alternative distributions based on the type of tanker used. The use of type 2 (3,500) tankers results in a minimum total distribution cost with the number of routes and tankers used is 2. Keywords: SDVRP, Palgunadi Algorithm, Multiple Products
ANALISIS PRIORITAS PASIEN COVID-19 UNTUK RAWAT INAP MENGGUNAKAN LOGISTIC REGRESSION DAN AHP Prasetio, Dimaz Arno; Zein, Hamada; Kusrini, Kusrini; Supriatin, Supriatin
CSRID (Computer Science Research and Its Development Journal) Vol 13, No 3 (2021): CSRID OKTOBER 2021
Publisher : Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid.13.3.2021.149-157

Abstract

Kondisi bertambahnya pasien covid-19 ini tidak sebanding dengan jumlah kamar yang tersedia di rumah sakit atau tempat khusus yang ditunjuk sebagai tempat isolasi pasien covid-19. Kekurangan ruang isolasi dan fasilitas juga dialami oleh rumah sakit lainnya diseluruh wilayah Indonesia. Sedikitnya jumlah kamar yang tersedia pada rumah sakit dan terbatasnya jumlah dan tenaga para dokter dan perawat maka diperlukan sebuah sistem untuk membantu dokter memberikan rekomendasi perangkingan pasien yang dapat masuk sebagai pasien rawat inap sehingga pasien, penggunaan ruangan, fasilitas serta tenaga yang ada menjadi lebih efisien serta mampu menolong semua pasien yang benar-benar membutuhkan. Dengan pemodelan yang diajukan AHP dapat membantu memberikan keputusan yang cepat dan dengan algoritma logistic regression mampu membantu mempercepat keputusan dari salah satu kriteria pada AHP yang digunakan dengan tingkat akurasi pengklasifikasian kondisi paru paru pasien sebesar 97.14%.
Co-Authors Adiatma, Biva Candra Lutfi Arief, M. Rudyanto Arief, Rudyanto Hamada Zein Hanif Al Fatta Ilkham, Sundari Khoironi, Khoironi Muahidin, Zumratul Nurcahyo, Azriel Christian Prasetio, Dimaz Arno Profesi, Dwinda Etika Saifudin, Saifudin Supriatin Supriatin Wati, Vera Wulandari, Sri