Abstract-Mobile based culinary recommendation system has become critical topic in mobile application. Some methods presented in the literature propose the use of the AHP (Analytic Hierarchy Process), AHP TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and fuzzy AHP for mobile based culinary recommendation system. However, there are no comparative studies of these three methods when applied to mobile based culinary recommendation system. Thus, this research presents a comparative analysis of these three methods in the context of culinary recommendation system in mobile environment. The comparison was made based on accuracy and time complexity because mobile application environment needs low time complexity. The results have shown that all of these methods are suitable for culinary recommendation system in mobile environment. Fuzzy AHP have the highest accuracy between all of these methods, it have 66,67 % accuracy. But, AHP TOPSIS shows the best performance in time complexity, with order of growth in quadratic class (n 2 ).

Abstract-Mobile based culinary recommendation system has received significant attention in recent mobile application research . Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) has regained popularity in supporting multi-criteria decision making due to this method allowing inclusion of many factors and criteria into the decision making process. Previous works on mobile based scenario culinary recommendation system reveal that TOPSIS stand out from other recommendation approaches like AHP and Fuzzy by providing a lightweight computation algorithm that have promising performance in time complexity. However, computing a culinary recommendation using TOPSIS has own limitations especially in the menu judgment processes due to the alternatives priority only include personal preferences for recommendation. In such a culinary recommendation system scenario, users more likely search culinary menus in group instead of alone. This research aims to develop a culinary recommendation system based on group decision support system (GDSS) using TOPSIS that possible to calculate a recommendation by using group preferences instead of personal preferences. The experimental results show that the overall functional of proposed GDSS gives better recommendation result. GDSS using TOPSIS have 100% rank consistency for 6 group of users with 5 combination of menus. The accuracy testing shows that 83,33 % recommendation of GDSS TOPSIS are match with real user preferences. Furthermore, it can be run well in various type of Android smartphone.

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.

Fitur yang digunakan untuk mengenali jenis daun meliputi bentuk, warna, dan tekstur. Tidak semua jenis fitur perlu digunakan untuk melakukan komputasi hasil ektraksi, 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, manhattan, euclidean, cosine, dan mahalanobis. Hasil penelitian ini menunjukkan nilai temu kembali paling tinggi ketika menggunakan seleksi fitur CFS dengan pengukuran kedekatan mahalanobis.

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.

About 15% of sugarcane leaf is defective because of diseases, it reduces the quantity and quality of sugarcane production significantly. Early detection and estimation of plant disease is a way to control these diseases and minimize the severe infection. This paper proposes a model to identify the severity of certain spot disease which appear on leaves based on segmented spot. The segmented spot is obtained by thresholding a* component of L*a*b* color space. Diseases spots are extracted with maximum standard deviation of segmented spot that use for detection the type of disease using classification techniques. The classifier is a Support Vector Machine (SVM) which uses L*a*b* color space for its color features and Gray Level Co-Occurrence Matrix (GLCM) as its texture features. This proposed model capable to determine the types of spot diseases with accuracy of 80% and 5.73 error severity estimation average.

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, 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%.

No abstract