KONTROL OPTIMASI MODEL EPIDEMIK HOST-VECTOR DENGAN SIMULASI MENGGUNAKAN FORWARD-BACKWARD SWEEP

Model epidemik host-vector dengan transmisi langsung diperluas dengan tiga pengendalian, yaitu kontrol pencegahan untuk meminimalkan kontak antara host-vektor, kontrol insektisidauntuk vector, dan kontrol pengobatan pada host yang terinfeksi. Tujuannya adalah untuk memperoleh strategi pencegahan yang optimal dengan biaya minimal. Karakterisasi kontrol optimal dilakukan secara analitik dengan menerapkan prinsip Minimum Pontryagin. Sistem kontinu yang diperoleh kemudian diselesaikan secara numerik dengan Forward-Backward Sweep Method untuk menyelidiki upaya pengendalian yang efektif dalam meminimalkan kejadian infeksi antara host-vector

Typo handling in searching of Quran verse based on phonetic similarities

The Quran search system is a search system that was built to make it easier for Indonesians to find a verse with text by Indonesian pronunciation, this is a solution for users who have difficulty writing or typing Arabic characters. Quran search system with phonetic similarity can make it easier for Indonesian Muslims to find a particular verse.  Lafzi was one of the systems that developed the search, then Lafzi was further developed under the name Lafzi+. The Lafzi+ system can handle searches with typo queries but there are still fewer variations regarding typing error types. In this research Lafzi++, an improvement from previous development to handle typographical error types was carried out by applying typo correction using the autocomplete method to correct incorrect queries and Damerau Levenshtein distance to calculate the edit distance, so that the system can provide query suggestions when a user mistypes a search, either in the form of substitution, insertion, deletion, or transposition. Users can also search easily because they use Latin characters according to pronunciation in Indonesian. Based on the evaluation results it is known that the system can be better developed, this can be seen from the accuracy value in each query that is tested can surpass the accuracy of the previous system, by getting the highest recall of 96.20% and the highest Mean Average Precision (MAP) reaching 90.69%. The Lafzi++ system can improve the previous system

Implementasi Keras Library dan Convolutional Neural Network Pada Konversi Formulir Pendaftaran Siswa

Form sheet is an instrument to collect someone’s information and in most cases it is used in a registration or submission process. The challenge being faced by physical form sheet (e.g. paper) is how to convert its content into digital form. As a part of study of computer vision, Optical Character Recognition (OCR) recently utilized to identify hand-written character by learning pattern characteristics of an object. In this research, OCR is implemented to facilitate the conversion of paper-based form sheet's content to be stored properly into digital storage. In order to recognize the character's pattern, this research develops training and testing method in a Convolutional Neural Network (CNN) environment. There are 262.924 images of hand-written character sample and 29 paper-based form sheets from SDN 01 Gumilir Cilacap that implemented in this research. The form sheets also contain various sample of human-based hand-written character. From the early experiment, this research results 92% of accuracy and 23% of loss. However, as the model is implemented to the real form sheets, it obtains average accuracy value of 63%. It is caused by several factors that related to character's morphological feature. From the conducted research, it is expected that conversion of hand-written form sheets become effortless.Lembar formulir merupakan salah satu media dalam mengumpulkan informasi seseorang dan umum digunakan pada proses registrasi/pendaftaran. Penggunaan media ini cukup mudah, namun hambatan yang kerap dihadapi dari penggunaan lembar formulir berbentuk fisik (misal: kertas) adalah pemindahan konten ke dalam bentuk digital. Optical Character Recognition (OCR) merupakan salah satu segmen dalam disiplin ilmu pengolahan citra yang dapat melakukan pengenalan karakter tulisan dengan mempelajari pola karakteristik suatu objek. Pada penelitian ini, OCR diimplementasikan pada keras library untuk memfasilitasi konversi konten tulisan tangan yang ada di sebuah kertas formulir sehingga dapat disimpan ke dalam media penyimpanan digital. Dalam pola pengenalan karakter, dikembangkan model pelatihan dan pengujian dengan menggunakan Convolutional Neural Network (CNN). Data yang digunakan pada penelitian adalah 262.924 karakter sampel tulisan tangan dalam bentuk citra dan 29 sampel kertas formulir dari SDN 01 Gumilir Cilacap. Model dengan akurasi tertinggi memperoleh nilai 92% dan loss sebesar 23%. Sedangkan, pada hasil pengujian menggunakan 29 formulir didapatkan nilai akurasi rata-rata sebesar 63%.  Hal ini disebabkan oleh beberapa faktor yang berkaitan dengan faktor morfologi dari citra tiap karakter huruf. Dari penelitian yang dilakukan diharapkan dapat mempermudah proses dokumentasi informasi dari lembar formulir tulisan tangan ke dalam media penyimpanan digital

Sistem Rekomendasi Pemilihan Peminatan Menggunakan Density Canopy K-Means

The carelessly selection of specialization course leaves some students with difficulty. Therefore, it is needed a recommendation system to solve it. Several approaches could be used to build the system, one of them was K-Means. K-Means required the number of initial centroid at random, so its result was not yet optimal. To determine the optimal initial centroid, Density Canopy (DC) algorithms had been proposed. In this research, DC and K-Means (DCKM) was implemented to build the recommendation system in the problem. The alpha criterion was also proposed to improve the performance of DCKM. The academic quality dataset in the 2018 informatics programs students of ITTP was used. There were three main stages in the system, namely determination of the weight of the course in dataset, implementation of DCKM, and determination of specialization recommendations. The results showed that the system by using DCKM has good quality based on the Silhouette results (at least 0.655). The system also used standar valuation scale in ITTP and silhouette index in the process of system. The results showed that 176 (65.91%) students were recommended in IT specialization, 25 (9.36%) students were recommended in MM specialization and 66 (24.7%) students were recommended in SC specialization.  Adanya mahasiswa yang kesulitan saat menjalani mata kuliah peminatan merupakan akibat dari pemilihan peminatan tanpa didasarkan pada data. Oleh karena itu dibutuhkan sistem rekomendasi pemilihan peminatan yang didasarkan pada data. Beberapa pendekatan telah dilakukan salah satunya dengan Clustering K-Means. K-Means membutuhkan jumlah dan letak centroid awal secara acak sehingga hasil clustering belum tentu optimal. Beberapa ilmuwan telah mengusulkan algoritme dalam penentuan centroid awal salah satunya adalah algoritme Density Canopy. Paper ini mengimplementasikan algoritme Density Canopy dan K-Means (DCKM) dalam membangun sistem rekomendasi pemilihan peminatan. Penambahan kriteria  (alpha) diusulkan pada proses DCKM untuk meningkatkan kinerja algoritme tersebut. Data yang digunakan adalah data mutu akademik semester 1 dan 2 mahasiswa Prodi Informatika Angkatan 2018 ITTP. Sistem rekomendasi yang dibangun memiliki tiga tahapan utama yaitu (1) penentuan bobot mata kuliah pada dataset, (2) implementasi DCKM dan (3) penentuan rekomendasi peminatan. Hasil penelitian menunjukkan bahwa sistem yang dibangun memiliki kualitas yang baik berdasarkan perolehan hasil Silhouette minimal 0.6552. Skala penilaian institusi dan derajat keanggotaan index Silhouette  juga dilibatkan sehingga mengurangi unsur subjektifitas. Hasil sistem rekomendasi menunjukkan 176 (65.91%) mahasiswa direkomendasikan pada peminatan TI, 25 (9.36%) mahasiswa direkomendasikan pada peminatan MM dan 66 (24.7%) mahasiswa direkomendasikan pada peminatan SC

KONTROL OPTIMAL MODEL PENYEBARAN VIRUS KOMPUTER DENGAN PENGARUH KOMPUTER EKSTERNAL YANG TERINFEKSI DAN REMOVABLE STORAGE MEDIA

.  In this paper, we discuss an optimal control on the spread of computer viruses under the effects of infected external computers and removable storage media. Prevention Strategies do with ascertaining control prevention to minimize the number of infective computers (Latent and Breakingout) and installing effective antivirus programs in each sub-population. The aim are to derive optimal prevention strategies and minimize the cost associated with the control. The characterization of optimal control is perform analitically by applying Pontryagin Minimum Principle. The obtained optimality system of Hamilton fuction is satistfy the optimality condition