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Yeni Kustiyahningsih
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Informatics Department, Engineering Faculty University of Trunojoyo Madura Jl. Raya Telang - Kamal, Bangkalan 69162, Indonesia Tel: 031-3012391, Fax: 031-3012391
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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 157 Documents
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THE ALGORITHM OF GIVING AUTOMATIC HARAKAT ON ARABIC SCRIPT Dini Hidayatul Qudsi; Maksum Ro’is Adin Saf
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.v9i2.145

Abstract

The majority of Arabic reference books in Arabic writing without a vowel/harakat causestudents to have difficulty in learning Arabic. Reading Arabic Script without harakatrequires an understanding of the science of Nahwu-Sharf (Arabic Grammar) to be able tounderstand its contents. Therefore, in this study the basic rules of Nahwu-Sharf Sciencehave been translated into an algorithm that can be used to provide an automatic harakaton Arabic script (without harakat). Based on the interview results with the expert, sixexamples of sentences that represented the types(conditions) of basic sentences in theArabic language have been used as scenarios to examine the generated algorithm. Theallowed sentence is limited to one sentence only and cannot be in the form of poetry orsatire. Additionally, Cyclomatic Complexity Testing is used to examine scenarios in a testcase. All the steps of the Cyclomatic Complexity have been performed, which are creatingthe flowgraph, calculating the independent path, and testing scenario. The scenarios wereexamined in a test case through 13-path test case examination and result match withexpected output. In addition, 24 examples which represented the six conditions have beenutilized to examine for more detailed analysis and also results accuracy 100% inaccordance with the basic rules of Nahwu-Sharf science.
APPLICATION OF HYBRID GA-PSO TO IMPROVE THE PERFORMANCE OF DECISION TREE C5.0 Achmad Zain Nur; Hadi Suyono; Muhammad Aswin
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.v10i4.248

Abstract

Data mining is a data extraction process with large dimensions and information with the aim of obtaining information as knowledge to make decisions. Problems in the data mining process often occur in high-dimensional data processing. The solution to handling problems in high-dimensional data is to apply the hybrid genetic algorithm and particle swarm optimization (HGAPSO) method to improve the performance of the C5.0 decision tree classification model to make decisions quickly, precisely and accurately on classification data. In this study, there were 3 datasets sourced from the University of California, Irvine (UCI) machine learning repositories, namely lymphography, vehicle, and wine. The HGAPSO algorithm combined with the C5.0 decision tree testing method has the optimal accuracy for processing highdimensional data. The lymphography and vehicle data obtained an accuracy of 83.78% and 71.54%. The wine dataset has an accuracy of 0.56% lower than the conventional method because the data dimensions are smaller than the lymphography and vehicle dataset.
IDENTIFIKASI SINYAL ELEKTRODE ENCHEPALO GRAPH UNTUK MENGGERAKKAN KURSOR MENGGUNAKAN TEKNIK SAMPLING DAN JARINGAN SYARAF TIRUAN Hindarto -; Moch. Hariadi; Mauridhi Hery Purnomo
Jurnal Ilmiah Kursor Vol 6 No 1 (2011)
Publisher : Universitas Trunojoyo Madura

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Abstract

This paper describe the application of backpropagation neural networks as classification and sampling technique (ST) for the extraction of features from the signal wave Electro Encephalo Graph (EEG). This research aims to develop a system that can recognize the EEG signal that is used to move the cursor. The data used is the EEG data which is IIIA dataset of BCI competition III (BCI Competition III 2003). This data contains data from three subjects: K3b, K6b and L1b. In this study, EEG signal data separated by the imagination of movement to the left, right, leg movements and tongue movements. Decision making has been carried out in two stages. In the first stage, TS is used to extract features from EEG signal data. This feature is as basic inputs in back propagation neural networks as a process of learning. This research used Back Propagation (20-20-10-5-1) and 90 data files EEG signal for the training process. During the identification process into four classes of EEG signal data files data files plus 60 into 150 EEG signal so that the EEG signal data file. The results obtained for the classification of these signals is 80% of the 150 files examined data signal to the process of mapping.
HIDDEN MARKOV MODELS BASED INDONESIAN VISEME MODEL FOR NATURAL SPEECH WITH AFFECTION Endang Setyati; Mauridhi Hery Purnomo; Surya Sumpeno; Joan Santoso
Jurnal Ilmiah Kursor Vol 8 No 3 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i3.61

Abstract

In a communication using texts input, viseme (visual phonemes) is derived from a group of phonemes having similar visual appearances. Hidden Markov model (HMM) has been a popular mathematical approach for sequence classification such as speech recognition. For speech emotion recognition, a HMM is trained for each emotion and an unknown sample is classified according to the model which illustrate the derived feature sequence best. Viterbi algorithm, HMM is used for guessing the most possible state sequence of observable states. In this work, first stage, we defined system of an Indonesian viseme set and the associated mouth shapes, namely system of text input segmentation. The second stage, we defined a choice of one of affection type as input in the system. The last stage, we experimentally using Trigram HMMs for generating the viseme sequence to be used for synchronized mouth shape and lip movements. The whole system is interconnected in a sequence. The final system produced a viseme sequence for natural speech of Indonesian sentences with affection. We show through various experiments that the proposed, the results in about 82,19% relative improvement in classification accuracy.
KWH METER IMAGE ENHANCEMENT USING COLOR SPACE TRANSFORMATION FOR IMPROVING CHARACTER SEGMENTATION ACCURACY Shinta Puspasari; Lastri Widya Astuti
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.v8i4.113

Abstract

This study proposes an approach to enhance image quality of power meter (KWH meter) using a color space transformation. Power meter is used to measure the power consumption of the customer. The amount of usage can be seen by looking at the character of numbers as indicators of measurement. Billing can be done automatically using character recognizer on imaging device by applying digital image processing techniques. Acquired image of the power meter may have poor quality because data acquisition process is very sensitive to light and noise. Appliance power meter is covered with glass that can reflect light, so that the quality of the acquired image varies depends on the lighting conditions at the time of acquisition. Color space transformation widen the color contrast of KWH meter image. The performance of the proposed approach is evaluated using a data set of KWH meter images of Smart Meter Indonesia models contains 30 RGB color model images. Before performs the proposed method, segmentation effectivesess is 93%. The experimental results shows an improvement of image quality that affect the character segmentation results up to 97%. Color space transformation is proven effective for the improvement of image quality and segmentation of KWH meter.
Optimization of Daylight Factor Distribution Using Standard Deviations Based on Shifting Window Position Yose Rizal; Imam Robandi; Eko Mulyanto Yuniarno
Jurnal Ilmiah Kursor Vol 10 No 3 (2020)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v10i3.229

Abstract

Natural lighting is an important factor that affects the comfort of building users. Natural lighting in a room requires a window area of ​​at least 1/6 of the floor area. This study was conducted to obtain the distribution of Daylight Factor (DF) as a natural lighting factor during the day in the room, based on the shift in the position of the window on the wall. The distribution of lighting entering the depth of the room through window openings is a tool to compare the best window position in the spread of illumination with DF calculations based on Sky Component (SC). Shifting the window position will be analyzed by Standard Deviation (S) and Mean (μ) based on the DF distribution. Optimizations of the DF distribution on the window position shifts if it has the largest DF mean value and the smallest DF variant value. The results of the study in a simple room showed that the optimal DF distribution was at the window position in the middle and the mean value was 2.59%. The relationship of shifting window position and DF distribution can be useful for architects to determine the function of a room in architectural design.
METODE ASSOCIATION RULE UNTUK ANALISIS CITRA CT ORGAN PASIEN KANKER OVARIUM Dwina Kuswardani; M. Rahmat Widyanto; Indang Trihandini
Jurnal Ilmiah Kursor Vol 6 No 2 (2011)
Publisher : Universitas Trunojoyo Madura

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Abstract

Penelitian ini melakukan analisis organ liver, ginjal dan uterus pasien kanker ovarium ada citra Computed Tomography (CT) scan. Analisis dari ketiga organ tersebut menggunakan Association Rule. Metode Association Rule merupakan bagian dari proses data mining yang bertujuan untuk menemukan kecenderungan suatu data, dalam hal ini dapat menemukan pola ciri organ liver, ginjal, dan uterus dari penderita kanker ovarium. Pembentukan Association Rule terdiri dari tiga tahap, yaitu ekstraksi fitur berdasarkan fitur ukuran dan intensitas organ, transaksi basis data, dan diakhiri penerapan Association Rule. Uji coba dilakukan terhadap tujuh belas data pasien kanker ovarium dari Rumah Sakit Kanker di Jakarta. Hasil dari Asociation Rule ditemukan bahwa ukuran uterus mempunyai pola ciri dengan support rule sebesar 55 %, pola ciri hubungan organ liver dan ukuran organ uterus dengan support rule 45 %, pola ciri hubungan organ liver, intensitas ginjal dan ukuran organ uterus dengan support rule 40 %, pola ciri hubungan organ intensitas ginjal dan ukuran uterus dengan support rule 50 %. Penerapan metode Association Rule untuk analisis citra CT scan organ pasien kanker ovarium ini diharapkan dapat membantu ahli medis dalam melakukan diagnosis. Kata Kunci: Association Rule, Computed Tomographi (CT), Citra Abdominal dan Pelvis. Abstract This research work on analysis of the organs liver, kidney anduterus, ovarian cancer patients at the image of Computed Tomography (CT) scan. Analysis of these organs use the Association Rule. Method of Association Rule is part of the process of data mining which aims to find the tendency of the data in this case can find a pattern characteristic of organs liver, kidney, and uterus of patients with ovarian cancer. Formation of Association Rule comprises three stages: feature extraction based on feature size and intensity of the organ, the transaction database, and terminated the application of Association Rule. Tests conducted on 17 patients with ovarian cancer data from cancer hospital in Jakarta. The result of Association Rule is found that uterus size has a characteristic pattern with support of rule 55 %, a pattern characteristic of relationship between liver organ and uterus size with support of rule 45 %, pattern characteristic of relationship between liver, kidney intensity and uterus size with support of rule 40 %, pattern characteristic of organ relationship kidney intensity and uterus size with support of rule 50 %. Application of Association Rule method for CT image analysis organ ovarian cancer patients is expected to help medical experts in making the diagnosis.
DETERMINING THE ABNORMALITY OF BULL SPERM TAIL MORPHOLOGY USING SUPPORT VECTOR Stevanus Hardiristanto; I Ketut Eddy Purnama,; Adhi Dharma Wibawa; Mira Candra Kirana; Budi Santoso; Munawir .; Slamet Hartono; I Nyoman Tirta Ariana; Dian Ratnawati; Lukman Affandhy
Jurnal Ilmiah Kursor Vol 7 No 2 (2013)
Publisher : Universitas Trunojoyo Madura

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Abstract

DETERMINING THE ABNORMALITY OF BULL SPERM TAIL MORPHOLOGY USING SUPPORT VECTOR a Stevanus Hardiristanto, b I Ketut Eddy Purnama, cAdhi Dharma Wibawa, dMira Candra Kirana, eBudi Santoso, fMunawir, g Slamet Hartono, h I Nyoman Tirta Ariana, iDian Ratnawati, jLukman Affandhy a,b,c,d,e,fDepartment of Multimedia and Network Engineering, Faculty of Industrial Technology, Institute of Technology Sepuluh Nopember, Surabaya, Indonesia gBalai Pembibitan Ternak Unggul Sapi Bali, Ministry of Agriculture, Republik of Indonesia h Faculty of Animal Science, University of Udayana, Bali, Indonesia i,jLoka Penelitian Sapi Potong Grati, Ministry of Agriculture, Republik of Indonesia E-mail: a hardi@its.ac.id Abstrak Penilaian atas ketidaknormalan spermatozoa bisa dilakukan dari sisi motilitas maupun morfologi (kepala dan ekor). Penelitian ini mengevalusi ketidaknormalan spermatozoa dari sisi morfologi bagian ekor spermatozoa sapi. Data berupa 50 citra mikroskopis spermatozoa yang diperoleh dari Loka Penelitian Sapi Potong Grati, Pasuruan digunakan dalam penelitian ini. Prosedur yang ditetapkan terdiri atas beberapa tahap. Tahap pertama adalah melakukan segmentasi spermatozoa untuk memisahkan spermatozoa dari latar belakang dan memisahkan bagian ekor spermatozoa dari bagian yang lain. Selanjutnya dari hasil segmentasi dicari garis tengah ekor (skeleton) menggunakan metode medial axis transform. Berdasarkan garis tengah yang dihasilkan, dilakukan prosedur ekstraksi fitur menggunakan metode polynomial curve fitting. Kemudian, metode Support Vector Machine (SVM) digunakan untuk menentukan ketidaknormalan bentuk ekor spermatozoa. Untuk pembelajaran digunakan 25 data spermatozoa normal dan 10 data spermatozoa tidak normal. Testing kemudian dilakukan atas 15 data spermatozoa tersisa. Ketelitian SVM dalam menentukan ketidaknormalan bentuk ekor spermatozoa mencapai 73.33%. Dengan demikian ketidaknormalan bentuk ekor spermatozoa dapat ditentukan dengan menggunakan SVM. Kata kunci: Ekor Sperma sapi, Morphology, Polynomial Curve Fitting, SVM. Abstract Determinining the abnormality of spermatozoa can be done by inspecting its motility or morphology (head or tail). This study examined 50 data of sperm microscopic images. The semen was obtained from Loka Penelitian Sapi Potong Grati, Pasuruan. A sequence of procedure consist of several steps were then carried out. The first step was to obtain sperm tails by segmenting the sperms from its background and removing the heads and the necks parts. The skeletons of the tails were then obtained using a method of medial axis transform. The features of the tails were then extracted using polynomial curve fitting. Then, Support Vector Machine (SVM) was used as a classifier. In the training phase, 25 normal sperm and 10 abnormal sperm were utilized. Afterward, the remaining 15 data were used in the testing phase. The accuracy of SVM was 73.33%. Hence, the abnormality of spermatozoa based on the shape of sperm tail can be determined using SVM. Key words: Bull Sperm Tail, Morphology, Polynomial Curve Fitting, SVM
PERILAKU TAKTIS UNTUK NON - PLAYER CHARACTERS DI GAME PEPERANGAN MENIRU STRATEGI MANUSIA MENGGUNAKAN FUZZY LOGIC DAN HIERARCHICAL FINITE STATE MACHINE Supeno Mardi Susiki Nugroho; Yunifa Miftachul Arif; Mochamad Hariadi; Mauridhi H Purnomo
Jurnal Ilmiah Kursor Vol 6 No 1 (2011)
Publisher : Universitas Trunojoyo Madura

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Abstract

Nowadays, the proliferation of game technology especially in intelligent human-like NPCs (Non-Player Characters) leads to more adaptive behavior of NPCs maneuvers. Providing smoother behaviors require comprehensive rules base which can respond to players behaviors. Addressing this requirement we propose NPCs which had tactical behaviors based on fuzzy logic. The fuzzy logic defines four type behaviors for the NPCs, which depend on NPC health, ammo, and distance of the enemy.Those behaviors implemented on two intelligent agents employed Hierarchical Finite State Machine to express the maneuver actions of NPC during combat scenes. Using First Person Game Engine, the performance of NPCs with fuzzy behavior compared with NPC without fuzzy behavior. The results of experiment showed the performance of the NPC with Fuzzy behavior outperform 80% better than the NPC without fuzzy behavior.
IMPACT OF IMPUTATION ON CLUSTER-BASED COLLABORATIVE FILTERING APPROACH FOR RECOMMENDATION SYSTEM Noor Ifada; Susi Susanti; Mulaab
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.v10i1.201

Abstract

The Collaborative Filtering (CF) widely used in Recommendation System commonly suffers the sparsity issue since the unobserved rating entries usually over dominance the observed ones. A clustering technique is an alternative solution that can solve the problem. However, no in-depth work has investigated how the missing entries should be mitigated and how the cluster-based approach can be implemented. In this study, we show how the imputed cluster-based approach deals with the missing entries, improving the recommendation quality. The framework of our method consists of four main stages: rating imputation to replace the missing entries, K-means clustering to group users or items based on the imputed rating data, CF-based prediction model, and generating the list of top-N recommendation. This paper uses three variations of imputation techniques, i.e., null, mean, and mode. The cluster-based approach is employedby using the K-Means as the clustering technique, and either the user-based or the items-based model as the CF approach. Experiment results show that the null imputation technique gives the best results when dealing with the missing entries. This finding indicates that the implementation of the clustering techniqueis sufficient for solving the sparsity issue such that imputing the missing entries is not necessary. We also show that our imputed cluster-based CF methods always outperform the traditional CF methods in terms of the F1-Score metric.

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