ASYNCHRONOUS ISLAND MODEL GENETIC ALGORITHM FOR UNIVERSITY COURSE TIMETABLING

ABSTRAKSI: Penjadwalan merupakan masalah kombinatorial kompleks yang diklasifikasikan sebagai NP - Hard . Masalah penjadwalan kuliah universitas (MPKU) mirip dengan masalah penjadwalan pada umumnya dengan beberapa bagian yang unik. MPKU adalah permasalahan penjadwalan dimana kita harus melakukan penjadwalan untuk pertemuan perkuliahan ke dalam slot waktu dan ruang tertentu dengan mempertimbangkan batasan keras (hard constrain) dan lunak (soft constraint) . Telkom University memiliki masala h yang hampir mirip dengan penjadwalan tersebut. Solusi saat ini dengan informed genetic algorithm untuk Telkom Universit y MPKU masih memiliki masalah waktu eksekusi .sland Model Genetic Algorithm digunakan dalam tesis ini untuk memecahkan masalah terseb ut . Ide tesis ini adalah membuat model pertukaran Individu untuk men distribusikan i ndividu lokal terbaik sebuah pulau dengan pulau lain. Island Model GA dapat membuat jadwal kuliah universitas dalam waktu yang masih dapat dipertimbangkan. Model terdistribusi ini dapat berjalan lebih cepat daripada model tunggal menurunkan pelanggaran batasan untuk mencapai nilai fitness yang optim um . Hal ini dapat terjadi karena model ini dapat keluar dari optimum lokal dengan lebih mudah. Island Model GA bahkan dapat menghasilkan akurasi yang baik untuk dataset Universitas Telkom (99,74%) dan akurasi yang cukup untuk dataset Purdue (96,80%) pada penjadwalan level mahasiswa.Kata Kunci : penjadwalan , penjadwalan kuliah universitas , informed genetic algorithm, island model genetic algorithmABSTRACT: Timetabling is a complex combinatorial problem classified as NP - Hard. University course timetabling problem (UCTP) is similar to other timetabling problems with some additional unique parts. UCTP involves assigning lecture events to timeslots and rooms subject to a variety of hard and soft constraints. Telkom University has almost similar problem with it s course timetabling. The current solution with Informed Genetic Algorithm for Telkom University UCTP still has the time consuming problem.sland Model informed Genetic Algorithm was used in this thesis to solve this problem. The idea of this thesis is m aking distributed model exchanges an island‟s local best Individu with another island. Island model GA could create university course timetabling in reasonable time. This distributed model could run faster rather than single machine model decreasing constr aint violations to reach optimum fitness. It could have less constraint violations because it could escape from stagnant local optimum easier. Island model GA could even produced great accuracy for Telkom University dataset ( 99.74% ) and acceptable accuracy at 96.80% for Purdue dataset for student level timetabling .Keyword: timetabling, university course timetabling problem, informed genetic algorithm, island model genetic algorith

Power Station Engine Failure Early Warning System Using Thermal Camera

Power station engines are critical infrastructure components that require constant monitoring to prevent failures and ensure an uninterrupted power supply. This paper proposes a failure early warning system based on a thermal camera using a computer vision approach. The system uses a thermal camera to generate thermal images in a video format, which is then processed by an automated fire detection engine and temperature detection engine. The results of these two subsystems are then used as input for an anomaly detection engine, which predicts the likelihood of engine failure. Based on the results of the experiments, it can be concluded that the YOLOv7 model outperforms Faster R-CNN in detecting fires, achieving a higher mAP score on the one-class dataset. The proposed temperature and anomaly detection system also accurately detected temperature levels and anomalies in thermal images. Furthermore, in the failure time prediction experiment, the Holt-Winters additive method with additive errors, additive trend, and additive seasonality model was identified as the best fit among the models evaluated. In contrast, the Decision Tree model showed good performance and a short training time, making it a good choice for applications where training time is critical. These results highlight the importance of selecting the most suitable method for a given application. Moreover, it demonstrates the effectiveness of different models and approaches for engine failure early warning systems in a power station using a thermal camera

Reinforced Island Model Genetic Algorithm to Solve University Course Timetabling

The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning teaching event in certain time and room by considering the constraints of university stakeholders such as students, lecturers, departments, etc. This problem becomes complicated for universities which have immense number of students and lecturers. Therefore, a scalable and reliable timetabling solver is needed. However, current solvers and generic solution failed to meet several specific UCTP. Moreover, some universities implement student sectioning problem with individual student specific constraints. This research introduces the Reinforced Asynchronous Island Model Genetic Algorithm (RIMGA) to optimize the resource usage of the computer. RIMGA will configure the slave that has completed its process to helping other machines that have yet to complete theirs. This research shows that RIMGA not only improves time performance in the computational execution process, it also oers greater opportunity to escape the local optimum trap than previous model

Analisis Penggunaan Metode Hidden Markov Model dalam Ekstraksi Kalimat Utama Suatu Dokumen pada Information Retrieval<br /><br />Analysis of Hidden Markov Model Method Implementation in Documents Topic Sentence Extraction for Information Retrieval

ABSTRAKSI: Pencarian dokumen di Internet memiliki karakteristik khusus yang harus dipertimbangkan yaitu bandwidth atau kecepatan akses yang terbatas serta waktu pencarian relatif lebih lambat daripada pencarian di desktop. Karena itu perlu dilakukan indexing pada proses Information Retrieval agar dapat mempercepat dan mempermudah pencarian. Makin banyak term yang terindeks akan makin membutuhkan waktu ekstra untuk mencari sebuah term. Sehingga diperlukan metode khusus untuk memangkas jumlah term dalam indeks. Salah satunya dengan melakukan ekstraksi dokumen menggunakan algoritma Hidden Markov Model. Metode yang dipakai dalam sistem ekstraksi ini adalah dengan melakukan pendekatan statistik dan HMM Hedge sebagai model HMM.Metode yang digunakan tersebut mengeluarkan hasil: penggunaan tagging dapat memangkas waktu ekstraksi dan jumlah term terindeks secara signifikan, parameter alpha pada proses decoding mencapai nilai optimum pada 0,2 dan 0,3, ekstraksi dapat mengurangi waktu proses indexing dan jumlah term yang terindeks, serta jenis corpus mempengaruhi nilai akurasi dari sistem ekstraksi.Kata Kunci : Hidden Markov Model, indexing, Information Retrieval, ekstraksiABSTRACT: Document searching in the Internet has special characteristic must be considered. Those are bandwith or limited access speed and searching time spending much longer rather than desktop searching. Therefore, it needs to use indexing at Information Retrieval process that can increase speed and simply searching activities. More indexing terms mean more extra time to searching any term. It needs special methods to cut the indexing terms. One of them is document extraction with Hidden Markov Model. The method using in this extraction system is statistical approach and HMM Hedge for the HMM Model.That method outputs results: tagging can reduce extraction and nnumer of indexed terms signicantly, alpha parameter in decoding reach optimum value in 0,2 and 0,3, extraction can reduce indexing time and number of indexed terms, and corpus kinds influence extraction system accuracy.Keyword: extraction, Hidden Markov Model, indexing, Information Retrieva

島モデル遺伝的アルゴリズムの多様性維持機能に関する研究

早大学位記番号:新8367早稲田大

Perancangan Modul Bahasa Indonesia untuk Microsoft FAST ESP Search Engine

Fungsi utama dari search engine atau mesin pencari adalah mempermudah dan mempercepat pencarian. Latar belakang dibuatnya mesin pencari ini adalah banyaknya jumlah dokumen di internet yang mempersulit proses pencarian user.Selain digunakan di internet, mesin pencari juga banyak diterapkan pada perusahaan.Mesin pencari untuk perusahaan sudah ada banyak, salah satunya adalah FAST ESP Search Engine.FAST ESP adalah sebuah software terintegrasi yang menyediakan sebuah platform untuk layanan pencarian dan filtering. Sistem ini dibuat terdistribusi sehingga dapat menyediakan temu balik informasi dari beberapa jenis informasi (dan dataset).FAST ESP secara otomatis mengenali 81 jenis bahasa termasuk Indonesia namun jika ingin mengaplikasikan FAST ESP untuk dokumen berbahasa Indonesia, penambahan modul custom harus dilakukan.Proses perancangan modul bahasa Indonesia untuk FAST ESP adalah analisis proses Microsoft FAST ESP, analisis strategi awal perancangan modul bahasa Indonesia, perancangan model linguistik bahasa Indonesia, dan studi prioritas proses pada model lingustik bahasa Indonesia. Pada akhirnya environment dan proses dalam FAST ESP sangat menentukan posisi dari modul bahasa Indonesia yang akan dimasukkan. Berdasarkan hasil analisis didapatkan bahwa tahap pipeline adalah satu-satunya tahap yang dapat digunakan. Proses penyisipan modul bahasa Indonesia adalah dengan mengganti atau menambahkan stage pada pipeline. Stage yang akan disisipi atau bahkan diubah dapat dibagi berdasarkan kebergantungannya kepada bahasa

Solving University Course Timetabling Problem Using Multi-Depth Genetic Algorithm

The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning teaching event in certain time and room by considering the constraints of university stakeholders such as students, lecturers, and departments. The constraints could be hard (encouraged to be satisfied) or soft (better to be fulfilled). This problem becomes complicated for universities which have an immense number of students and lecturers. Moreover, several universities are implementing student sectioning which is a problem of assigning students to classes of a subject while respecting individual student requests along with additional constraints. Such implementation enables students to choose a set of preference classes first then the system will create a timetable depend on their preferences. Subsequently, student sectioning significantly increases the problem complexity. As a result, the number of search spaces grows hugely multiplied by the expansion of students, other variables, and involvement of their constraints. However, current and generic solvers failed to meet scalability requirement for student sectioning UCTP. In this paper, we introduce the Multi-Depth Genetic Algorithm (MDGA) to solve student sectioning UCTP. MDGA uses the multiple stages of GA computation including multi-level mutation and multi-depth constraint consideration. Our research shows that MDGA could produce a feasible timetable for student sectioning problem and get better results than previous works and current UCTP solver. Furthermore, our experiment also shows that MDGA could compete with other UCTP solvers albeit not the best one for the ITC-2007 benchmark dataset

Virtual Reality to Promote Tourism in Indonesia

Abstract— Tourism is an important source of income for many countries, especially in Indonesia. Indonesia is famous for its archipelago and tropic climate islands. Many tourists have visited Indonesia for leisure, business, and other purposes. To attract and increase potential tourists to travel to Indonesia, a promoting activities need to be done. However, these activities can only be done through a conventional flat display of a television, computer, or a smartphone. The information perceived from this kind of display only brings that much. The potential tourist can only “see” the advertisement, without feeling any interest in it. With the help of Virtual Reality Technology, an immersive advertisement about tourism can be created. Using the Oculus Rift DK2, this paper will explain how to integrate the promoting activities of tourism with the Virtual Reality Technology to create an immersive virtual world of Tourism to attract a potential tourist to visit Wonderful Indonesia.Tourism is an important source of income for many countries, especially in Indonesia. Indonesia is famous for its archipelago and tropic climate islands. Many tourists have visited Indonesia for leisure, business, and other purposes. To attract and increase potential tourists to travel to Indonesia, a promoting activities need to be done. However, these activities can only be done through a conventional flat display of a television, computer, or a smartphone. The information perceived from this kind of display only brings that much. The potential tourist can only “see” the advertisement, without feeling any interest in it. With the help of Virtual Reality Technology, an immersive advertisement about tourism can be created. Using the Oculus Rift DK2, this paper will explain how to integrate the promoting activities of tourism with the Virtual Reality Technology to create an immersive virtual world of Tourism to attract a potential tourist to visit Wonderful Indonesia

Asynchronous island model genetic algorithm for university course timetabling

University course timetabling problem (UCTP) is similar to general timetabling problems with some additional unique parts. UCTP involves assigning lecture events to timeslots and rooms subject to a variety of hard and soft constraints. Telkom University has almost similar problem with its course timetabling. The current solution with Informed Genetic Algorithm for Telkom University UCTP still has the time consuming problem. Island Model informed Genetic Algorithm was used in this research to solve this problem. The idea of this research is making distributed model exchanges an island’s local best Individu with another island. Island model GA could create university course timetabling in reasonable time. This distributed model could run faster rather than single machine model decreasing constraint violations to reach optimum fitness. It could have less constraint violations because it could escape from stagnant local optimum easier. Island model GA could even produce great accuracy for Telkom University dataset (99.74%) and acceptable accuracy at 96.80% for Purdue dataset for student level timetabling.</p