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Yeni Kustiyahningsih
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INDONESIA
Jurnal Ilmiah Kursor
Published by Universitas Trunojoyo Madura
ISSN : 02160544     EISSN : 23016914     DOI : https://doi.org/10.21107/kursor
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal Ilmiah Kursor is published in January 2005 and has been accreditated by the Directorate General of Higher Education in 2010, 2014, 2019, and until now. Jurnal Ilmiah Kursor seeks to publish original scholarly articles related (but are not limited) to: Computer Science. Computational Intelligence. Information Science. Knowledge Management. Software Engineering. Publisher: Informatics Department, Engineering Faculty, University of Trunojoyo Madura
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 155 Documents
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THE IMPLEMENTATION OF HEART RATE SENSOR AND MOTION SENSORS BASED ON INTERNET OF THINGS FOR ATLETE PERFORMANCE MONITORING Sritrusta Sukaridhoto; Muhammad Aksa Hidayat; Achmad Basuki; Riyadh Arridha; Andi Roy; Titing Magfirah; Agus Prasetyo; Udin Harun Al Rasyid
Jurnal Ilmiah Kursor Vol 10 No 1 (2019)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v10i1.208

Abstract

Indonesian achievements in the ASEAN Games continued to decline in achievement starting in 1962 with the acquisition of 51 medals and up to 2014 with the acquisition of 20 medals. The decline in achievement was due to the lack of athletic resources due to the absence of media that could record athletes' abilities in the field. Can record the athlete's performance before running, running and after running using the Heart Rate sensor and Motion Capture sensor. The results of the sensor recording will be stored in the database. This system applies the Internet of Things (IoT) concept, using raspberry pi, Arduino microcontroller, T34 polar heart rate sensor to capture and send heartbeat to receivers, gyro-based motion-capture sensors that named wear notch where this sensor serves to capture the movement of athletes, sensors communicate with the system using 4G connectivity, use MQTT as edge computing which acts as a communication medium from sensors to databases, Maria DB and influx DB as accumulation which plays a role in storing heart rate and athlete's movements that have been recorded by sensors, athlete performance monitoring platform with a heart rate sensor and athlete's motion capture is a web-based application that collaborates all processes from the sensor to the system. Sensor heart rate recording results are categorized good because the error margin is only 0.4%. Wearnotch sensor data can be stored in the database, and athletic data can be recorded before sports, while sports, and after sports in real-time
MEASURING USER EXPERIENCE IN AN ONLINE STORE USING PULSE AND HEART METRICS Paulus Insap Santosa
Jurnal Ilmiah Kursor Vol 7 No 3 (2014)
Publisher : Universitas Trunojoyo Madura

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Abstract

MEASURING USER EXPERIENCE IN AN ONLINE STORE USING PULSE AND HEART METRICS Paulus Insap Santosa Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada Email: insap@ugm.ac.id Abstrak Beberapa sukses faktor toko daring dapat diringkas ke dalam elemen kebergunaan toko daring tersebut. Secara umum, kebergunaan berfokus pada kegunaan dan dapat digunakanya toko daring untuk membantu kustomer belanja secara daring. Akhir-akhir ini pengalaman positif pengguna ketika berbelanja secara daring menjadi tuntutan yang semakin nyata. Kebergunaan dan pengalaman pengguna adalah dua hal yang berbeda meskipun sangat berkaitan. Kebergunaan berfokus pada produk, dan pengalaman pengguna berfokus pada perasan dan emosi pengguna. Artikel ini melaporkan studi empiris untuk mengidentifikasi faktor yang berkontribusi pada pengalaman positif situs belanja daring. Responden berjumlah 121 yang merupakan mahasiswa yang belum pernah melakukan belanja daring. Para responden dihadapkan pada sebuah toko daring yang menjual beberapa barang. Mereka mengikuti skenario yang memungkinkan mereka melihat hampir semua fitur toko daring. Pengalaman pengguna diukur dengan menggunakan kombinasi metrik PULSE dan HEART dengan beberapa modifikasi untuk disesuaikan dengan keadaan. Analisis data menunjukkan bahwa responden mendapatkan manfaat yang lebih tinggi dibanding biaya yang harus ditanggung, dan kebahaguiaan dan sukses menjalankan tugas merupakan dua peubah yang memberikan pengaruh tertinggi kepada pengalaman pengguna. Kata kunci:pengalaman pengguna, kebergunaan, toko daring, PULSE, HEART, scenario Abstract Several success factors of online store can be summarized as usability. In general, usability focuses on how useful and usable the online store toward helping customers in doing their online shopping. Recently, more demand towards user positive experience becomes apparent. Usability and user experience are two different things but closely related. Usability focuses on products, and user experience focuses on user’s feelings and emotion. This paper reports an empirical study to determine factors contribute to positive experience in an online store success. There were 121 respondents who were students who had never done online shopping. They were exposed to a mockup online store selling several merchandises. They followed certain scenario that allowed them experiencing most online store features. User experience was measured using a combination of PULSE and HEART metrics with some modification to suit the current condition. Data analysis showed that respondents gained more benefit compared to the incurred cost, and happiness and task success were two variables provided more influence to user experience. Keywords: user experience, usability, online store, PULSE, HEART, scenario.
A FUZZY TIME SERIES-MARKOV CHAIN MODEL TO FORECAST FISH FARMING PRODUCT Bagus Dwi Saputra; Rachmat Gernowo; Budi Warsito
Jurnal Ilmiah Kursor Vol 9 No 4 (2018)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i4.167

Abstract

Price is one of the important things that need to concern as defining factor of the profit or loss of product selling as the result of price fluctuations that are very difficult to control. Price fluctuations are caused by many factors including weather, stock availability, demand and others. One of the steps to solve the price fluctuations problem is by making a forecast of fish incoming prices. The purpose of this study is to apply Markov chain’s fuzzy time series to forecast farming fish prices. Markov chain fuzzy time series is one of the prediction methods to predict time series data that has advantages in the implentation of historical data, flexible, and high level of data forecasting accuracy. This study used fish prices at November 2018. The results showed that markov chain fuzzy time series showed very accurate forecasting results with a mean error percentage of absolute percentage error (MAPE) of 1.4% so the accuracy of the Markov chain fuzzy time series method is 98, 6%.
RESTRICTED CONTENT CLASSIFICATION BASED ON VIDEA METADATA AND COMMENTS (CASE STUDY : YOUTUBE.COM) Stefanus Thobi Sinaga; Masayu Leylia Khodra
Jurnal Ilmiah Kursor Vol 7 No 4 (2014)
Publisher : Universitas Trunojoyo Madura

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Abstract

RESTRICTED CONTENT CLASSIFICATION BASED ON VIDEA METADATA AND COMMENTS (CASE STUDY : YOUTUBE.COM) aStefanus Thobi Sinaga, aMasayu Leylia Khodra a,bSekolah Teknik Elektro dan Informatika, Institut Teknologi Bandung, Jl. Ganesha 10 Bandung E-Mail: s.thobi.sinaga@gmail.com Abstrak Klasifikasi konten terbatas merupakan kegiatan memisahkan konten video yang layak untuk seluruh pengguna dari konten yang tidak layak untuk pengguna di bawah umur (<18 tahun). Pada situs Youtube, proses klasifikasi konten terbatas dilakukan secara manual oleh karyawan berdasarkan laporan yang dikirimkan oleh komunitas pengguna. Pada penelitian ini dirancang sebuah sistem klasifikasi konten terbatas secara otomatis yang dapat melakukan klasifikasi terhadap video Youtube berdasarkan teks metadata (judul, deskripsi) dan komentar dari video tersebut. Sistem tersebut memanfaatkan model klasifikasi hasil eksperimen terhadap dataset video Youtube yang telah dikumpulkan. Judul dan deskripsi video dipilih sebagai atribut klasifikasi karena mengandung informasi utama yang ditulis oleh penggunggah terkait video yang diunggah. Sedangkan komentar dipilih sebagai atribut klasifikasi karena dapat dijadikan sumber informasi ketika informasi yang disediakan oleh pengunggah tidak dapat mereprentasikan video yang digunakan. Melalui eksperimen, didapatkan model klasifikasi dengan F-Measure sebesar 83,45%. Model dibangun dengan menggunakan pendekatan leksikal terhadap dataset judul dan deskripsi video (tanpa komentar), Support Vector Machines sebagai algoritma klasifikasi, serta metode binary sebagai metode pembobotan fitur. Dengan menggunakan model tersebut, telah dikembangkan sistem klasifikasi konten terbatas berdasarkan teks metadata dan komentar video. Kata kunci: Klasifikasi, Konten Terbatas, Support Vector Machines. Abstract Restricted content classification is an activity of labeling video content into two category, which are restricted content that is appropriate for all audiences and non-restricted content that are not appropriate for minor audiences (age < 18). On Youtube, restricted content classification is being processed manually by the expert staffs based on user reports. This research aims to build automatic restricted content classification system which is able to classify Youtube video based on its metadata (title, description) and video comments. This system would use the best model achieved from the experiment on Youtube video dataset. Video title and description are chosen as the classification attribute since they contain the main information about the video provided by the uploader. Meanwhile, video comments are chosen as the other classification attribute under the assumption that they would provide the information necessary when video title and description are not able to give any information related to the video. Our experiment shows that the best classification model with F-Measure of 83.45% is achieved by using lexical feature on dataset built from video title and description (without comments). We employed Support Vector Machines as the classification algorithm and binary as the feature weighting method. In this paper, a restricted content classification system based on metadata and video comments has been built. Keywords:Classification,Restricted Content, Support Vector Machines.
A MODIFIED PARTICLE SWARM OPTIMIZATION WITH RANDOM ACTIVATION FOR INCREASING EXPLORATION Alrijadjis Alrijadjis; Shenglin Mu; Kanya Tanaka; Shota Nakashima
Jurnal Ilmiah Kursor Vol 8 No 1 (2015)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i1.72

Abstract

Particle Swarm Optimization (PSO) is a popular optimization technique which is inspired by the social behavior of birds flocking or fishes schooling for finding food. It is a new metaheuristic search algorithm developed by Eberhart and Kennedy in 1995. However, the standard PSO has a shortcoming, i.e., premature convergence and easy to get stack or fall into local optimum. Inertia weight is an important parameter in PSO, which significantly affect the performance of PSO. There are many variations of inertia weight strategies have been proposed in order to overcome the shortcoming. In this paper, a new modified PSO with random activation to increase exploration ability, help trapped particles for jumping-out from local optimum and avoid premature convergence is proposed. In the proposed method, an inertia weight is decreased linearly until half of iteration, and then a random number for an inertia weight is applied until the end of iteration. To emphasis the role of this new inertia weight adjustment, the modified PSO paradigm is named Modified PSO with random activation (MPSO-RA). The experiments with three famous benchmark functions show that the accuracy and success rate of the proposed MPSO-RA increase of 43.23% and 32.95% compared with the standard PSO.
PREPROCESSING WITH SYMMETRICAL FACE AND GAMMA CORRECTION FOR FACE RECOGNITION UNDER VARYING ILLUMINATION WITH ROBUST REGRESSION CLASSIFICATION Eva Y Puspaningrum; Budi Nugroho; Andri Istifariyanto
Jurnal Ilmiah Kursor Vol 9 No 2 (2017)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i2.142

Abstract

Facial recognition is one of the most popular issues in the field of pattern recognition.Face recognition with uncontrolled lighting conditions is more significant than thephysical characteristics of individual faces. Uncontrolled lighting from the right and leftcan affect the face image. A lot of research on facial recognition, but little attention givento the face image is symmetrical object. Several studies to explore and exploit thesymmetrical properties of the face for face recognition were performed. In this paper, wepropose a pre-processing method to solve one of the common problems in facial imageswith varying illumination. We utilize the symmetric property of the face then performedgamma correction then classified using Robust Regression. The results of this experimentgot an average accuracy of 94.31% and the proposed technique improves recognitionaccuracy especially in images with extreme lighting conditions using gamma correctionparameters γ = 0.3.
PLANNING OF 5G NETWORK PATH LOSS IN GEOMETRY BASED STOCHASTIC CONCEPT BY USING LINEAR REGRESSION METHODS Achmad Ubaidillah; S. Ida Kholida
Jurnal Ilmiah Kursor Vol 10 No 4 (2020)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v10i4.245

Abstract

This research is a continuation of several previous studies that made 5G network planning using the Free Space Reference Path Loss model. In this study, a 5G network path loss planning was made using the Geometry Based Stochastic model. A forecasting system is created that connects the path loss with the distance between the transmitter and the receiver antenna using the linear regression method. It is important to look at 5G network planning on a different side. The result shows that the path loss value in the light of sight condition is better than the non-light of sight condition with the lowest value of 94.4271 dB at the frequency of 28 GHz and 99.5856 dB at the 73 GHz frequency. Linear Regression analysis shows that the best path loss calculation is the frequency 28 GHz of LOS conditions with MSE is 0.001 and the standard deviation error is 0.0319.
ADAPTIVE DATA CLUSTERING METHOD BASED ON ARTIFICIAL BEE COLONY AND K-HARMONIC MEANS I Made Widiartha; Agus Zainal Arifin; Anny Yuniarti
Jurnal Ilmiah Kursor Vol 6 No 3 (2012)
Publisher : Universitas Trunojoyo Madura

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Abstract

ADAPTIVE DATA CLUSTERING METHOD BASED ON ARTIFICIAL BEE COLONY AND K-HARMONIC MEANS a I Made Widiartha, b Agus Zainal Arifin, c Anny Yuniarti a Jurusan Ilmu Komputer, FMIPA, Universitas Udayana Kampus Bukit, Gedung BJ Lt.I, Jimbaran Bali, b,c Informatics Department, Faculty of Information Technology Institute of Technology Sepuluh Nopember E-Mail: a imdewidiartha@cs.unud.ac.id Abstrak Berbagai metode telah dibuat untuk dapat melakukan klasterisasi data. Salah satu metode tersebut adalah K-Harmonic Means Clustering (KHM). KHM merupakan metode klasterisasi data yang menyempurnakan K-Means Clustering (KM). Metode KHM telah mampu mengurangi permasalahan KM dalam hal sensitifitas pada inisialisasi titik pusat awal, meskipun demikian dalam KHM masih terdapat kemungkinan solusi yang dihasilkan merupakan suatu lokal optimal. Permasalahan lokal optimal ini dapat diatasi dengan memanfaatkan suatu metode yang memiliki karakteristik pencarian solusi global ke dalam metode KHM. Artificial Bee Colony (ABC) merupakan suatu metode swarm yang berbasis pada perilaku mencari makan dari koloni lebah madu yang memiliki karakteristik untuk menghindari kemungkinan konvergensi terhadap lokal optimal. Dalam penelitian ini diusulkan sebuah metode baru untuk klasterisasi data yang berbasis pada metode ABC dan KHM (ABC-KHM). Kinerja metode ABC-KHM ini telah dibandingkan dengan metode KHM dan ABC dengan memanfaatkan lima dataset. Dari hasil penelitian didapatkan hasil dimana metode ABC-KHM ini telah berhasil mengoptimalkan posisi titik pusat klaster KHM yang mengarahkan hasil klaster menuju suatu solusi global. Kata kunci: K-Means Clustering, K-Harmonic Means Clustering, Artificial Bee Colony, ABC-KHM. Abstract Various methods have been made to cluster the data. One such method is K-Harmonic Means Clustering (KHM). KHM is a clustering method that improves K-Means Clustering (KM). KHM method was able to reduce the problem of KM in terms of sensitivity to the initialization of the initial center point nevertheless there is still a possibility that the result of KHM is a local optimum. The local optimal problem can be solved by utilizing a method that has characteristic of a global search into KHM method. Artificial Bee Colony (ABC) is a swarm method based on foraging behavior of honey bee colony that has characteristics to avoid the possibility of local optimum convergence. In this research, a new method for data clustering based on ABC and KHM (ABC-KHM) is proposed. The performance ABC-KHM method has been compared with ABC and KHM by using five datasets. The results show that ABCKHM method is able to optimize the position of the cluster center and directs the center to a global solution. Key words: K-Means Clustering, K-Harmonic Means Clustering, Artificial Bee Colony, ABC-KHM.
DESIGN OF POTENTIAL CELLULASE PRIMER USING MULTIPLE SEQUENCE ALIGNMENT METHOD Bahrul Ulum; Wisnu Ananta Kusuma; Joni Prasetyo
Jurnal Ilmiah Kursor Vol 7 No 1 (2013)
Publisher : Universitas Trunojoyo Madura

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Abstract

DESIGN OF POTENTIAL CELLULASE PRIMER USING MULTIPLE SEQUENCE ALIGNMENT METHOD aBahrul Ulum, bWisnu Ananta Kusuma, c Joni Prasetyo a,bDepartment of Computer Science, Bogor Agricultural University, Bogor, Indonesia aDepartment of Informatics Engineering, Al-Kamal Institute of Science and Technology, Jakarta cRenewable Energy Division, BPPT, Serpong, Indonesia E-mail: aabahrul@gmail.com Abstrak Selulase mempunyai peranan utama dalam pemanfaatan limbah biomassa yang mengandung lignin, hemicellulose, dan cellulose (lignocellulose). Limbah biomassa ini sangat banyak terdapat di lingkungan dan sampai saat ini masih belum dimanfaatkan secara maksimal, dikarenakan banyak mikroorganisme dari alam yang memproduksi enzim selulase dengan jumlah terbatas (aktifitasnya rendah). Dalam rangka meningkatkan produktivitas mikroorganisme untuk menghasilkan selulase, salah satu cara yang dapat diterapkan adalah merancang primer sekuens gen penyandi selulase yang dirangkum dari beberapa mikroorganisme penghasil selulase. Dalam penelitian ini, kami melakukan penyejajaran sekuen DNA penyandi selulase untuk mencari potensial primer untuk meningkatkan produktivitas enzim selulase dengan teknik Multiple Sequence Alignment (MSA). Metode yang digunakan adalah metode progresif (Progressive Alignment Algorithms). Hasil penelitian menunjukan bahwa pada tahap penyejajaran, didapatkan tiga daerah konservatif (conserved regions). Sedangkan pada tahap perancangan dengan beberapa parameter yang telah ditentukan didapatkan 46 pasang primer dari lima sekuen gen penyandi selulase yang didapat dari National Center for Biotechnology Information (NCBI). Kata kunci: Selulase, Multiple Sequence Alignment, Perancangan Primer. Abstract The role of cellulase is very important in degrading cellulose which is abundant in the environment, such as in biomass waste that is containing lignin, hemicellulose, and cellulose. Biomass waste is abundant in the environment and is still not fully utilized, because many of the natural microorganisms that produce cellulase enzymesproduce the enzyme in a limited amount (have low activity). In order to improve the productivity of microorganisms in producing cellulase, one of the ways that can be applied is to design primer sequences of genes encoding cellulase summarized from several cellulase-producing microorganisms. In this research,we perform alignment of DNA sequences coding of cellulase to look for potential primer in order to increase the productivity of cellulase enzymes by Multiple Sequence Alignment (MSA) method. The method used is progressive (Progressive Alignment Algorithms). The results showed that in the alignment phase, three conserved regions were obtained. However, in the planning phase by using some predetermined parameters 46 pairs of primer sequences were obtained from five genes encoding cellulase taken from NCBI. Keywords: Cellulase, Multiple Sequence Alignment, Primer design.
MEDICINAL PLANT SPECIES IDENTIFICATION SYSTEM USING TEXTURE ANALYSIS AND MEDIAN FILTER Prihastuti Harsani; Arie Qurania; Triasti nurmiatiningsih
Jurnal Ilmiah Kursor Vol 8 No 4 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i4.112

Abstract

Identification of plants can be done through objects - objects in plants by asking an expert or through a specimen (herbarium) that have been identified previously. Identification is done by matching the pictures in the book of flora or monograph. Computer-aided identification can be done using digital image processing methods which utilize digital image matching object plant with a picture on the book. Identification key that is used is the image of the leaves. This study develops previous research has identified using the method of fractal and Euclidian Distance. Accuracy obtained in each of the identification system for the fractal dimension and fractal code is of 68% and 51%. Improved accuracy is the main objective of this study. The proposed method is a method of texture analysis and median filter. Texture analysis is used as feature extraction technique while the median filter is image enhancement techniques. Based on the trials, the results of the identification of texture analysis method and median filter to increase to 78%. Median filter is used as a technique to improve the image quality leaves. The use of an identification system to be tested in the web application of information systems of medicinal plants.

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