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Identifikasi Penyakit pada Daun Tebu dengan Gray Level Co-Occurrence Matrix dan Color Moments Dewi, Ratih Kartika; Ginardi, R.V. Hari
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 1, No 2 (2014)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (819.699 KB)

Abstrak Karat dan mosaik adalah penyakit pada tebu yang menyerang tebu di Indonesia dan menimbulkan kerugian. Teknologi informasi untuk deteksi penyakit tebu diperlukan dalam menunjang peningkatan produksi tebu yang dapat menghasilkan panen optimal. Penelitian yang berkembang dalam identifikasi penyakit tanaman melalui identifikasi citra digital daun belum ada yang khusus membahas tebu, tetapi mengenai penyakit tanaman secara umum. Penelitian ini membangun sistem identifikasi penyakit pada daun tebu melalui identifikasi citra digital daun dengan pemilihan fitur tekstur dan warna melalui gray level co-occurrence matrix (GLCM) dan color moments. Tahap awal penelitian adalah pengumpulan data citra daun tebu berpenyakit dari survei lapangan. Tahap selanjutnya adalah pre-processing citra untuk dapat diolah ke tahap selanjutnya yaitu ekstraksi fitur. Ekstraksi fitur tekstur dilakukan dengan gray level co-occurrence matrix (GLCM) dan ekstraksi fitur warna dengan color moments. Klasifikasi dilakukan berdasarkan fitur yang telah diekstraksi sebelumnya. Penelitian ini menggunakan metode klasifikasi support vector machine (SVM). Pengujian dilakukan untuk mengetahui fitur yang kemunculannya menyebabkan perubahan dalam hasil klasifikasi dengan 4 skenario meliputi penghapusan fitur bentuk, pemilihan fitur tekstur, pemilihan fitur warna, dan kombinasi fitur tekstur dan warna. Kombinasi fitur tekstur dengan GLCM correlation, energy,Â homogeneity dan variance bersama fitur warna dengan color moments 1,2 dan 3 yang diuji pada skenario 4 merupakan kombinasi fitur yang direkomendasikan untuk identifikasi penyakit pada daun tebu dengan akurasi 97%. Kata kunci: ekstraksi fitur, penyakit tebu, citra daun, GLCM, dan color moments. Abstract Mosaic and rust are sugarcane diseases that happen in Indonesia and has considerable economic impact. Information technology for sugarcane disease detection is useful in supporting optimal sugarcane production. Most of current researches are about plant disease identification in general. There is no specific research about identification of sugarcane disease. This research proposes a sugarcane disease identification from sugarcane leaf image with gray level co-occurrence matrix (GLCM) and color moments. This research begins with collecting data from field survey. After sugarcane leaf images are captured through a field survey, they are pre-processed in order to be used in the features extraction step. Extracted features from these images are texture and color. Texture feature extraction is conducted by GLCM while color feature extraction is conducted by color moments. Classification method which is used in this research is support vector machine (SVM). Test conducted to find distinctive feature that has a significant impact in classification, there are 4 scenario to test the effects in deletion of shape feature, selection of texture and color feature, and also combination of texture and color feature. Texture feature with GLCM correlation, energy,Â homogeneity and variance combined with color moments 1, 2 and 3 for color feature extraction in 4th scenario is an appropriate feature for identification of sugarcane leaf disease with 97% classification accuracy. Keywords: feature extraction, sugarcane disease, leaf image, GLCM and color moments.

SELEKSI FITUR MENGGUNAKAN EKSTRAKSI FITUR BENTUK, WARNA, DAN TEKSTUR DALAM SISTEM TEMU KEMBALI CITRA DAUN Sari, Yuita Arum; Dewi, Ratih Kartika; Fatichah, Chastine
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 12, No 1, Januari 2014
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Fitur yang digunakan untuk mengenali jenis daun meliputi bentuk, warna, dan tekstur. Tidak semua jenis fitur perlu digunakan untuk melakukan komputasi hasil ektra ksi, namun perlu diseleksi beberapa fitur yang paling berpengarauh dalam sistem temu kembali citra daun. Teknik seleksi fitur Correlation based Featured Selection (CFS) digunakan untuk melakukan pemilihan fitur berdasarkan korelasi antar fitur, sehingga dapat meningkatkan performa dari sistem temu kembali citra daun. Jenis seleksi fitur yang digunakan diantaranya menggunaka CFS, CFS dengan Genetic Search (GS), dan chi square. Analisis keterkaitan korelasi antar fitur melalui seleksi fitur juga dikombinasikan dengan penggunaan kedekatan dalam menghitung similaritas pada sistem temu kembali. Penggunaan kedekatan dengan Lp norm, ma nhattan, euclidean, cosine, dan mahalanobis. Hasil penelitian ini menunjukkan nilai temu kembali paling tinggi ketika menggunakan seleksi fitur CFS dengan pengukuran kedekatan mahalanobis.

Deteksi Kanker Kulit Melanoma dengan Linear Discriminant Analysis-Fuzzy k-Nearest Neigbhour Lp-Norm Mentari, Mustika; Sari, Yuita Arum; Dewi, Ratih Kartika
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 2, No 1 (2016): Januari-Juni
Publisher : Prodi Sistem Informasi - Universitas Pesantren Tinggi Darul Ulum

Seiring perkembangan teknologi dilakukan otomatisasi deteksi kanker kulit melalui citra dermoscopy. Pengambilan informasi fitur citra dermoscopy terganggu dengan outlier dan overfitting, karena faktor jenis kulit, penyebaran kanker yang tidak merata atau kesalahan sampling. Penelitian ini mengusulkan deteksi kanker kulit melanoma dengan mengintegrasikan metode fuzzy K-Nearest Neighbour (FuzzykNN), Lp-norm dan Linear Discriminant Analysis (LDA) untuk mengurangi outlier dan overfitting. Masukan berupa citra warna RGB yang dinormalisasi menjadi RGBr. Reduksi dimensi dengan LDA menghasilkan fitur dengan nilai eigen paling menonjol. LDA pada penelitian ini menghasilkan dua fitur paling menonjol dari 141 jenis fitur, yaitu wilayah tumor dan minimum wilayah tumor channel R. Kemudian dilakukan klasifikasi FuzzykNN dan metode pengukur jarak Lp-norm. Penggunaan metode LDA dan Lp-norm dalam proses klasifikasi ini mengatasi terjadinya overfitting. Akurasi yang dihasilkan metode LDA-fuzzykNN Lp Norm, yaitu 72% saat masing-masing nilai p dan k = 25. Metode gabungan ini terbukti cukup baik dari pada metode yang dijalankan terpisah. Kata kunci: melanoma, fuzzy, KNN, Lp-norm, LDA.Â As the advancement of technology skin cancer detection need to be automated with the use of dermoscopy image. Outlier and overfitting are the problem in feature extraction of dermoscopy image, this can be caused by skin type, uneven cancer distribution or sampling error. This study proposed melanoma skin cancer detection by fuzzy K-Nearest Neighbour (FuzzykNN) with Lp-norm integrated with Linear Discriminant Analysis (LDA) to reduce the problem of outlier and overfitting. Input used in this study are images with RGB channel, then it adapted to RGBr. Dimensional reduction with LDA result in features with highest eigen value. LDA in this research select 2 discriminant, they are tumor area and minimum tumor area in R channel. This features then classified by fuzzykNN with Lp-Norm. Integration of LDA and Lp-norm in classification can reduce the problem of overfitting. This study results in 72% accuracy when the value of p and k are 25. Integration of LDA and fuzzykNN with Lp-norm has better result than unintegrated method. Keywords: melanoma, fuzzy, KNN, Lp-norm, LDA.

TOPSIS for mobile based group and personal decision support system Dewi, Ratih Kartika; Jonemaro, Eriq Muhammad Adams; Kharisma, Agi Putra; Farah, Najla Alia; Dewantoro, Mury Fajar
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 7, No 1 (2021): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v7i1.2140

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an algorithm that can be used for alternative design in a decision support system (DSS). TOPSIS provides recommendation so that users can get information that support their decision, for example a tourist wants to visit a tourist destination in Malang, then TOPSIS provides recommendations of tourist destinations in the form of ranking recommendation, with the highest rank is the most recommended recommendation. TOPSIS-based Mobile Decision Support System (DSS) has relatively low algorithm complexity. However, there are some cases that require development from personal DSS to group DSS, for example tourists rarely come alone, in which case most of them invite friends or family. For users who are more than 1 person, the TOPSIS algorithm can be combined with the BORDA algorithm. This study explains about the implementation & testing of TOPSIS and TOPSIS-BORDA as algorithms for personal and group DSS in mobile-based tourism recommendation system in Malang. Correlation testing was conducted to test the effectiveness of TOPSIS in mobile-based recommendation system. In previous study, correlation testing for personal DSS showed that there was a relationship between the recommendation and user choice, with correlation value of 0.770769231. In this study, correlation testing for group DSS showed there is a positive correlation of 0.88 between the recommendations of the group produced by TOPSIS-BORDA and personal recommendations for each user produced by TOPSIS.

Deteksi Kanker Kulit Melanoma dengan Linear Discriminant Analysis-Fuzzy k-Nearest Neigbhour Lp-Norm Mentari, Mustika; Sari, Yuita Arum; Dewi, Ratih Kartika
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 2, No 1 (2016): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v2i1.443

Seiring perkembangan teknologi dilakukan otomatisasi deteksi kanker kulit melalui citra dermoscopy. Pengambilan informasi fitur citra dermoscopy terganggu dengan outlier dan overfitting, karena faktor jenis kulit, penyebaran kanker yang tidak merata atau kesalahan sampling. Penelitian ini mengusulkan deteksi kanker kulit melanoma dengan mengintegrasikan metode fuzzy K-Nearest Neighbour (FuzzykNN), Lp-norm dan Linear Discriminant Analysis (LDA) untuk mengurangi outlier dan overfitting. Masukan berupa citra warna RGB yang dinormalisasi menjadi RGBr. Reduksi dimensi dengan LDA menghasilkan fitur dengan nilai eigen paling menonjol. LDA pada penelitian ini menghasilkan dua fitur paling menonjol dari 141 jenis fitur, yaitu wilayah tumor dan minimum wilayah tumor channel R. Kemudian dilakukan klasifikasi FuzzykNN dan metode pengukur jarak Lp-norm. Penggunaan metode LDA dan Lp-norm dalam proses klasifikasi ini mengatasi terjadinya overfitting. Akurasi yang dihasilkan metode LDA-fuzzykNN Lp Norm, yaitu 72% saat masing-masing nilai p dan k = 25. Metode gabungan ini terbukti cukup baik dari pada metode yang dijalankan terpisah. Kata kunci: melanoma, fuzzy, KNN, Lp-norm, LDA.Â As the advancement of technology skin cancer detection need to be automated with the use of dermoscopy image. Outlier and overfitting are the problem in feature extraction of dermoscopy image, this can be caused by skin type, uneven cancer distribution or sampling error. This study proposed melanoma skin cancer detection by fuzzy K-Nearest Neighbour (FuzzykNN) with Lp-norm integrated with Linear Discriminant Analysis (LDA) to reduce the problem of outlier and overfitting. Input used in this study are images with RGB channel, then it adapted to RGBr. Dimensional reduction with LDA result in features with highest eigen value. LDA in this research select 2 discriminant, they are tumor area and minimum tumor area in R channel. This features then classified by fuzzykNN with Lp-Norm. Integration of LDA and Lp-norm in classification can reduce the problem of overfitting. This study results in 72% accuracy when the value of p and k are 25. Integration of LDA and fuzzykNN with Lp-norm has better result than unintegrated method. Keywords: melanoma, fuzzy, KNN, Lp-norm, LDA.

Optimization of Healthy Diet Menu Variation using PSO-SA Imam Cholissodin; Ratih Kartika Dewi
Journal of Information Technology and Computer Science Vol. 2 No. 1: June 2017
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Abstract. Optimal healthy diet in accordance with the allocation of cost needed so that the level of nutritional adequacy of the family is maintained. The problem of optimal healthy diet (based on family budget) can be solved with genetic algorithm. The algorithm particle swarm optimization (PSO) has the same effectiveness with genetic algorithm but PSO is superior in terms of efficiency, PSO algorithm has a lower complexity than genetic algorithm. However, genetic algorithms and PSO have a problem of local optimum because these algorithm associated with random numbers. To overcome this problem, PSO algorithm will be improved by combining it with simulated annealing algorithm (SA). Simulated annealing algorithm is a numerical optimization algorithms that can avoid local optimal. From our results, optimal parameter for PSO-SA are popsize 280, crossover rate 0.6, mutation rate 0.4, first temperature 1, last temperature 0.2, alpha 0.9, and generation size 100.Keywords: PSO, SA, optimization, variation, healthy diet menu.

Usability Evaluation of Mobile-Based Application for Javanese Script Learning Media Ratih Kartika Dewi; Nurizal Dwi Priandani; Komang Candra Brata; Lutfi Fanani
Journal of Information Technology and Computer Science Vol. 3 No. 1: June 2018
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Indonesian people should actively preserve Indonesian culture. A way to preserve Indonesian culture can be done by using Javanese scripts as a local content subject at elementary to middle school level. In the conventional learning method, almost all teachers teach writing Javanese manuscript with conventional instructional media by using a white board. We proposed a mobile application that can help students to learn how to write Javanese script in attractive way by using their finger. Since this application still in prototype stage, further study and analysis of the usability of this application are necessary to validate the feasibility in real implementation. Usability testing using USE questionnaire had been conducted to find out if application of Javanese script writing can be accepted by users. We give 30 questions about usability to 5 respondents that are familiar with android application. The result show that, the proposed application is acceptable to users in term of Usefulness, ease of use, ease of learning and satisfaction.

Group Decision Support System based on AHP-TOPSIS for Culinary Recommendation System Ratih kartika dewi
Jurnal Ilmu Komputer dan Informasi Vol 12, No 2 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

This paper proposes the integration of AHP and TOPSIS to generate the ranking results of culinary recommendation for a group of users to provide better recommendation results. Formerly, Group Decision Support System (GDSS) for culinary recommendations has been developed with the TOPSIS method. TOPSIS has low algorithm complexity, so it is suitable to be applied in mobile devices. However, GDSS with TOPSIS has its disadvantages, TOPSIS have not been able to facilitate the preferences of each user inside a group so the recommendation result always consist only on dominant user. TOPSIS method produces unchanging rankings, because this method recommends a food menu based on the 1 dominant user so that the ranking is always consistent. Meanwhile, this study aims to integrate AHP for weighting criteria from each user and TOPSIS for ranking culinary recommendations. Based on rank consistency testing results that conducted in 6 different user groups, unlike the previous research, AHP-TOPSIS shows inconsistency ranking, which means that changes in user preferences affect the recommendation results that are generated by application. The AHP-TOPSIS method proved can be accommodated the computation of various preferences of each user in GDSS culinary recommendation

Forward Chaining for Contextual Music Recommendation System Ratih Kartika Dewi; M. Salman Ramadhan; Dwi Yovan Harjananto; Chindy Aulia Sari; Zumrotul Islamiah
Computer Engineering and Applications Journal Vol 10 No 3 (2021)
Publisher : Universitas Sriwijaya

Music is an important aspect of people's daily lives. The reasons people listen to music include to fill their free time and to keep the mood in good condition. Music recommendations are a recommendation system that exists not only because of the many types of music available, but also because people's perceptions of music are still not fully understood. But with so many music choices it makes it difficult for users to find music that fits their context. Examples include considering music based on the current user's location or current activities. A system is required that can recommend music in the context faced by the user.Music Recommendation System Development, Based on user context is a mobile application that uses the Android operating system. The recommendations provided by this system use expert system methods with forward chaining flow. The system will process inputs obtained from users and provide musical recommendations in accordance with the references provided by experts. The result of this study is a rule that is built to produce an average accuracy between user choice and system recommendations of 72%.

NPC Braking Decision for Unity Racing Game Starter Kit Using Naïve Bayes Muhammad Aminul Akbar; Tri Afirianto; Steven Willy Sanjaya; Ratih Kartika Dewi
Fountain of Informatics Journal Vol 4, No 2 (2019): November
Publisher : Universitas Darussalam Gontor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21111/fij.v4i2.3591

AbstractRacing video game genre was still being popular today. One way to develop racing games quickly is by using a template or kit that is on the game engine. Racing Game Starter Kit (RGSK) was being the most popular racing game template for Unity game engine. However, there was problem in racing game’s NPC especially in RGSK related to NPC vehicle’s braking decision. The commonly used method is the Brake Zone, but the developers must manually place the zone themselves in the designated locations for braking. The solution that can be applied for that problem is see the angle formed by the vector of the NPC vehicle with the vector from 2 next following waypoint then determine the best configuration angle threshold for NPC braking, but this also has its shortcoming in which to get the best result, a proper threshold configuration is needed in each track. To resolve the problem, researcher proposed the method of machine learning, Naïve Bayes for the braking decision. Naïve Bayes uses two output classes (brake or no brake) in which the data will be obtained from the player. We use data from players who can control racing car games well or have never hit a wall and have fast lap times. The purpose of this study is to provide an alternative braking method to RGSK that can provide fast lap times but does not affect the game's FPS and without the need to determine or change any parameters on each track. The test result using RGSK v1.1.0a in Unity Game Engine showed that the proposed method can be an alternative method in RGSK braking decisions. Our NPC has faster lap time and was able to prevent the vehicle from crashing with the outer wall without dropping the game’s FPS (Frames per Second).Keywords: Braking Decision, Racing Game Starter Kit, Naïve Bayes, Machine learning, Unity engine AbstrakGenre video gim balap masih populer saat ini. Salah satu cara untuk mengembangkan game balap dengan cepat adalah menggunakan template atau kit yang ada di game engine. Racing Game Starter Kit (RGSK) adalah templat game balap paling populer pada Unity Game Engine. Namun, terdapat permasalahan NPC pada gim balapan terutama di RGSK terkait dengan keputusan pengereman kendaraan NPC. Metode yang digunakan untuk eksperimen jenis ini adalah Zona Rem. Namun, pengembang harus secara manual menempatkan zona tersebut di lokasi tertentu pada setiap lintasan. Solusi dari masalah ini yang sudah diterapkan pada RGSK v1.1.0a yaitu dapat menggunakan sudut yang dibentuk oleh vektor kendaraan NPC dengan vektor dari 2 titik arah berikutnya, kemudian menentukan ambang sudut terbaik untuk pengereman NPC, tetapi ini juga memiliki masalah yaitu untuk mendapatkan hasil putaran terbaik atau cepat, perlu menentukan konfigurasi ambang batas yang tepat di setiap trek. Untuk mengatasi masalah tersebut, peneliti mengusulkan metode pembelajaran mesin, Naïve Bayes untuk keputusan pengereman. Naïve Bayes menggunakan dua kelas output (rem atau tidak ada mengerem) di mana data akan diperoleh dari pemain. Kami menggunakan data dari pemain yang dapat mengontrol permainan mobil balap dengan baik atau tidak pernah menabrak tembok dan memiliki waktu putaran yang cepat. Tujuan dari penelitian ini adalah untuk memberikan metode pengereman alternatif untuk RGSK yang dapat memberikan waktu putaran yang cepat namun tidak mempengaruhi FPS game dan tanpa perlu menentukan atau mengubah parameter apa pun di setiap trek. Hasil pengujian menggunakan RGSK v1.1.0a di Unity Game Engine menunjukkan bahwa metode yang diusulkan dapat menjadi metode alternatif dalam keputusan pengereman RGSK. NPC kami mempunyai waktu putaran yang lebih cepat dan mampu mencegah kendaraan agar tidak menabrak dinding luar tanpa menjatuhkan FPS game (Frame per Detik).Kata kunci: Keputusan Pengereman, Racing Game Starter Kit, Naïve Bayes, Pembelajaran Mesin, Unity Engine

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