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BLENDED LEARNING DARI PERSPEKTIF PARA GURU SEKOLAH DI PONDOK PESANTREN Muklason, Ahmad; Mahananto, Faizal; Anggraeni, Wiwik; Djunaidy, Arif; Riksakomara, Edwin
SISFO Vol 8 No 2 (2019)
Publisher : Department of Information Systems, Institut Teknologi Sepuluh Nopember

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To survive the industrial revolution 4.0 which was marked by the massive use of digital technology, educational organizations are expected to do digital transformation. However, unfortunately it is commonly known that there is a gap in the use of technology in Indonesia between big cities and regions, between modern education system and traditional education system. To reduce this gap, blended learning technology training was held aimed for teachers in the Darul Ulum Islamic boarding school in Peterongan, Jombang. Pondok Pesantren Darul Ulum Islamic is a boarding school with almost 10,000 students. In the Darul Ulum Islamic boarding school its self there are various educational units ranging from primary school to higher education. This study focuses on investigating the online learning technology adoption by teachers in Islamic boarding school, especially their perspective on the technology. Our findngs showed that they are very ready to adopt the technology.

PENDEKATAN HYPER HEURISTIC DENGAN KOMBINASI ALGORITMA PADA EXAMINATION TIMETABLING PROBLEM Supoyo, Vicha Azthanty; Muklason, Ahmad
ILKOM Jurnal Ilmiah Vol 11, No 1 (2019)
Publisher : Univeristas Muslim Indonesia

Generally, exam scheduling is still done manually and definitely will take a long time. Many researches have developed various studies to find a more appropriate strategy. Hyperheuristic was proposed in this study. In Hyperheuristic, Simple Random is used as a strategy for selecting low-level-heuristic while Hill Climbing and Simulation Annealing as move acceptance strategy. The Carter dataset is used as a test for the algorithms. We proposed testing datasets with a time limit of 15 minutes up to 1 hour and the results were compared with the research conducted by Carter et al (1996) as an initial study using that dataset. In addition, dataset, the number of iterations, and the time limit are as same as one of the literatures which will then be compared. The results obtained show that one pair of algorithms proposed in this study is better than the literature while other algorithms also provide significant results.

VIRTUAL CLASS SEBAGAI STRATEGI PEMBELAJARAN UNTUK PENINGKATAN KUALITAS STUDENT-CENTERED LEARNING DI PERGURUAN TINGGI Prassida, Grandys Frieska; Muklason, Ahmad
Teknologi: Jurnal Ilmiah Sistem Informasi Vol 1, No 2 (2011): July
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

ABSTRAK Metode pembelajaran berpusat pada siswa (Student-Centered Learning) memberikan ruang gerak lebih bagi mahasiswa untuk dapat berpartisipasi aktif dalam aktivitas perkuliahan di Perguruan Tinggi sesuai dengan kompetensi yang ingin dicapai. Melalui penerapan Student-Centered Learning ini maka mahasiswa dapat mengoptimalkan kemampuannya dalam belajar kreatif dan mandiri sehingga peran dosen dalam proses pembelajaran lebih diarahkan sebagai pendamping dan fasilitator belajar mahasiswa. Untuk mendukung penerapan Student-Centered Learning ini dapat dilakukan dengan memanfaatkan Information and Communication Technology (ICT) dalam berbagai macam strategi pembelajaran, salah satunya adalah dengan mengimplementasikan konsep kelas virtual (Virtual Class). Kata kunci : student-centered learning, information and communication technology, strategi pembelajaran, virtual class ABSTRACT Student Centered Learning (SCL) gives students some extra space for actively participating in every learning activity in the university according to their aiming competence. In this SCL application, student hopefully could optimize their ability in creative and self study learning so that lecturers are just stay as their study supervisor and facilitator. Information and Communication Technology (ICT) is then used to draw on this as a SCL booster in every learning strategy, such as implementing Virtual Class. Key words : student centered learning, information and communication technology, learning strategy, virtual class

PENGEMBANGAN SISTEM KLASIFIKASI PAPER OTOMATIS MENGGUNAKAN ALGORITMA BACK PROPAGATION NEURAL NETWORK PADA OPEN JOURNAL SYSTEM (OJS) Malays, Fajar Kurnia; Anggraeni, Wiwik; Muklason, Ahmad
Proceedings of KNASTIK 2010
Publisher : Duta Wacana Christian University

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Klasifikasi dokumen sebenarnya adalah permasalahan yang mendasar dan penting, dan berarti memasukkan suatu dokumen tertentu ke dalam kategori yang ada. Metode yang sekarang ada adalah melakukan tugas klasifikasi secara manual. Hal itu dirasa sangat menyulitkan dan melelahkan apabila dokumen yang ada berjumlah ratusan atau bahkan ribuan. Sehingga perlu dibuat sistem klasifikasi dokumen secara otomatis pada suatu permasalahan tertentu, misalnya pada makalah ini adalah pengklasifikasian dokumen pada OJS. Dalam makalah kali ini, penulis menggunakan algoritma Back Propagation Neural Network untuk memecahkan masalah klasifikasi dokumen. Data dokumen yang digunakan adalah 3 kategori yang diunduh dari jurnal online dengan masing-masing kategori berjumlah 100 dokumen. Untuk pembuatan sistem tersebut digunakan bahasa pemrograman PHP. Hasil menunjukkan bahwa aplikasi klasifikasi dokumen menunjukkan kebenaran pengujian data yang cukup tinggi, yaitu 91-94%. Sistem ini dapat membantu manajer atau pengelola forum (OJS) terutama dalam hal mengklasifikasikan suatu dokumen atau paper yang ada ke dalam kelas yang bersesuaian.

Why What they Say Matters: The Impacts of Visitorsâ€™ Experiences on Tourism Sustainability Suryani, Adi; Soedarso, Soedarso; Rahmawati, Deti; Endarko, Endarko; Muklason, Ahmad; Wibawa, Berto Mulia
International Journal of Social Science and Business Vol 5, No 1 (2021)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/ijssb.v5i1.31355

The success and failure of a destination tend to rest on itsâ€™ capacity to satisfy the visitors. Listening to visitorsâ€™ appraisal and voices is vital in community-based tourism development. This study aims to explore WPP Dalegan visitorsâ€™ voices. The data are collected through Dalegan visitorsâ€™ online reviews and direct observation. The study finds that Dalegan has a high competitive advantage as family recreational destination as it is cheap, reachable, accessible, has various local-traditional-cheap food, beautiful calm beach and soft white sand as a playing and learning ground for children. Despite itsâ€™ comfortable image, some visitors voice unsatisfied services, facilities, nature-caused and human-caused threats. The study also illuminates that Dalegan destination branding is not only influenced by itsâ€™ capacity to attract visitors by itsâ€™ beautiful marine nature, but also local community characters, community education and values. The study indicates that to develop tourism destination, local potentials are not the only determinant keys. It needs to be supported by other determining factors. One of those factors is touristsâ€™ voices on their impression, comments, satisfaction and dissatisfaction feelings. Linking potential resources of destination and touristsâ€™ meaningful experience can be challenging as different tourists may have different perspectives, wants and satisfaction-dissatisfaction levels.

VIRTUAL CLASS SEBAGAI STRATEGI PEMBELAJARAN UNTUK PENINGKATAN KUALITAS STUDENT-CENTERED LEARNING DI PERGURUAN TINGGI Prassida, Grandys Frieska; Muklason, Ahmad
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 1, No 2 (2011): July
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v1i2.55

ABSTRAK Metode pembelajaran berpusat pada siswa (Student-Centered Learning) memberikan ruang gerak lebih bagi mahasiswa untuk dapat berpartisipasi aktif dalam aktivitas perkuliahan di Perguruan Tinggi sesuai dengan kompetensi yang ingin dicapai. Melalui penerapan Student-Centered Learning ini maka mahasiswa dapat mengoptimalkan kemampuannya dalam belajar kreatif dan mandiri sehingga peran dosen dalam proses pembelajaran lebih diarahkan sebagai pendamping dan fasilitator belajar mahasiswa. Untuk mendukung penerapan Student-Centered Learning ini dapat dilakukan dengan memanfaatkan Information and Communication Technology (ICT) dalam berbagai macam strategi pembelajaran, salah satunya adalah dengan mengimplementasikan konsep kelas virtual (Virtual Class). Kata kunci : student-centered learning, information and communication technology, strategi pembelajaran, virtual class ABSTRACT Student Centered Learning (SCL) gives students some extra space for actively participating in every learning activity in the university according to their aiming competence. In this SCL application, student hopefully could optimize their ability in creative and self study learning so that lecturers are just stay as their study supervisor and facilitator. Information and Communication Technology (ICT) is then used to draw on this as a SCL booster in every learning strategy, such as implementing Virtual Class. Key words : student centered learning, information and communication technology, learning strategy, virtual class

Stock price forecast of macro-economic factor using recurrent neural network M. Reza Pahlawan; Edwin Riksakomara; Raras Tyasnurita; Ahmad Muklason; Faizal Mahananto; Retno A. Vinarti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp74-83

The stock market is one of the investment choices that always have traction from time to time. Aside from being a means of corporate funding, investing in the stock market can benefit investors. Investing also has a higher risk because the pattern of stock prices is volatile, which is caused by internal and external factors. One external factor that affects stock prices is the macro-economic, where these factors are events that occur in a country where one of the economic sectors affected is stock prices. Investors often feel confused about the right time in decisions making related to buying or selling stock. One way to look at how the prospect of stock prices is a stock price forecasting activity. For this study, we will be making use of the recurrent neural network (RNN) to forecast stock prices for the next periods. This research involves two variables: the closing stock price and the rupiah exchange rate against the dollar for the daily period. We achieve a MAPE value of 1.546% for RNN model without the variable foreign exchange rate and 1.558% for the RNN model that uses the foreign exchange rate against the dollar.

Perbandingan Metode Penyelesaian Permasalahan Optimasi Lintas Domain dengan Pendekatan Hyper-Heuristic Menggunakan Algoritma Reinforcement-Late Acceptance Anang Firdaus; Ahmad Muklason; Vicha Azthanty Supoyo
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8, No 5: Oktober 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021853263

Sebuah organisasi terkadang membutuhkan solusi untuk permasalahan optimasi lintas domain. Permasalahan optimasi lintas domain merupakan permasalahan yang memiliki karakteristik berbeda, misalnya antar domain optimasi penjadwalan, rute kendaraan, bin packing, dan SAT. Optimasi tersebut digunakan untuk mendukung pengambilan keputusan sebuah organisasi. Dalam menyelesaikan permasalahan optimasi tersebut, dibutuhkan metode pencarian komputasi. Di literatur, hampir semua permasalahan optimasi dalam kelas NP-hard diselesaikan dengan pendekatan meta-heuristics. Akan tetapi meta-heuristic ini memiliki kekurangan, yaitu diperlukan parameter tunning untuk setiap problem domain yang berbeda. Sehingga pendekatan ini dirasa kurang efektif. Oleh karena itu diperlukan pendekatan baru, yaitu pendekatan hyper-heuristics. Metode hyper-heuristic merupakan metode pencarian komputasi approximate yang dapat menyelesaikan permasalahan optimasi lintas domain dengan waktu lebih cepat. Lintas domain permasalahan yang akan diselesaikan ada enam, yaitu satisfiability (SAT), one dimensional bin packing, permutation flow shop, personnel scheduling, travelling salesman problem (TSP), dan vehicle routing problem (VRP). Dalam meningkatkan kinerja, penelitian ini menguji pengaruh dari adaptasi algoritma Reinforcement Learning (RL) sebagai strategi seleksi LLH dikombinasikan dengan algoritma Late Acceptance sebagai move acceptance, selanjutnya disebut algoritma Reinforcement Learning-Late acceptance (RL-LA). Untuk mengetahui efektivitas performa dari algoritma RL-LA, performa algoritma RL-LA yang diusulkan dibandingkan dengan algoritma Simple Random-Late Acceptance (SR-LA). Hasil dari penelitian ini menunjukan bahwa algoritma yang diusulkan, i.e. RL-LA lebih unggul dari SR-LA pada 4 dari 6 domain permasalahan uji coba, yaitu SAT, personnel scheduling, TSP, dan VRP, sedangkan pada domain lainnya seperti bin packing dan flow shop mengalami penurunan. Secara lebih spesifik, RL-LA dapat meningkatkan peforma pencarian dalam menemukan solusi optimal pada 18 instance dari 30 instance atau sebesar 64%, dan jika dilihat dari nilai median dan minimum metode RL-LA lebih unggul 28% dari metode SR-LA. Kontribusi utama dari penelitian ini adalah studi performa algoritma hibrida reinforcement learning dan late acceptance dalam kerangka kerja hyper-heuristics untuk menyelesiakan permasalahan optimasi lintas domain. AbstractAn organization sometimes needs solutions to cross domain optimization problems. The problem of cross domain optimization is a problem that has different characteristics, for example between domain optimization scheduling, vehicle routes, bin packing, and SAT. This optimization is used to support an organization's decision making. In solving these optimization problems, a computational search method is needed. In the literature, almost all optimization problems in NP-hard class are solved by meta-heuristics approach. However, this meta-heuristic has drawbacks, namely tuning parameters are needed for each different problem domain. So this approach is considered less effective. Therefore a new approach is needed, namely the hyper-heuristics approach. Hyper-heuristic method is an approximate computational search method that can solve cross domain optimization problems faster. In this final project there are six cross domain problems to be solved, namely satisfaction (SAT), one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman problem (TSP), and vehicle routing problem (VRP). In improving performance, this study examines the effect of the adaptation of the Reinforcement Learning (RL) algorithm as LLH selection combined with the Late Acceptance algorithm as a move acceptance. The results of this study indicate that there are 4 out of six problem domains that have improved performance, namely the SAT, personnel scheduling, TSP, and VRP, while in other domains such as bin packing and flow shop has decreased.

Identifikasi Faktor Resistansi Guru Terhadap Teknologi Sebagai Pendukung Pembelajaran di Pondok Pesantren Salaf Athiyatul Ulya; Feby Artwodini Muqtadiroh; Ahmad Muklason
Jurnal Nasional Teknologi dan Sistem Informasi Vol 7, No 1 (2021): April 2021
Publisher : Jurusan Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v7i1.2021.18-26

Sistem pendidikan di Indonesia terdiri dari beberapa jenis seperti pendidikan umum, kejuruan, profesi, dan keagamaan. Salah satu penyelenggara pendidikan keagamaan di Indonesia ialah pondok pesantren termasuk pondok pesantren salaf. Pondok pesantren salaf memiliki sistem pendidikan yang unik yakni sistem pembelajaran tradisional dengan metode sorogan, bandongan dan weton. Memasuki era Education 4.0, perkembangan teknologi pendidikan yang semakin canggih tidak lantas menjadikan pondok pesantren salaf beralih menggunakan teknologi. Pondok pesantren salaf tetap mempertahankan metode pembelajaran tradisional yang menjadi ciri khas pondok pesantren. Namun demikian, mereka tidak kalah saing dengan lembaga pendidikan lain serta terus mengalami perkembangan dengan jumlah santri yang meningkat setiap tahunnya. Penelitian ini bertujuan untuk menemukan faktor resistansi penggunaan teknologi sebagai pendukung pembelajaran dilihat dari perspektif guru. Penelitian yang menggunakan metode penelitian kualitatif ini terdiri dari 3 tahap yakni tahap persiapan, pengumpulan data dan pengolahan data. Pada tahap persiapan dilakukan studi literatur, identifikasi kondisi saat ini, menentukan populasi dan sampel serta menyusun instrumen penelitian. Tahap pengumpulan data diawali dengan pelaksanaan preliminary research, penyebaran kuesioner, pelaksanaan wawancara serta pengujian validitas dan reliabilitas data. Pengolahan data dilakukan dengan menentukan faktor dan kategori faktor resistansi guru terhadap teknologi. Hasil dari penelitian ini berupa faktor-faktor resistansi guru terhadap teknologi di pondok pesantren salaf. Faktor resistansi yang paling berpengaruh yakni faktor yang memiliki tingkat cakupan paling tinggi adalah kebijakan pesantren yang tidak memperbolehkan membawa barang elektronik, fasilitas pendukung teknologi yang tidak memadai serta rendahnya kemampuan teknis guru untuk mengoperasikan teknologi.

Complex University Timetabling Using Iterative Forward Search Algorithm and Great Deluge Algorithm I Gusti Agung Premananda; Ahmad Muklason
Khazanah Informatika Vol. 7 No. 2 October 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i2.12879

University timetabling is an issue that has received more attention in the field of operations research. Course scheduling is the process of arranging time slots and room for a class by paying attention to existing limitations. This problem is an NP-Hard problem, which means the computation time to find a solution increases exponentially with the size of the problem. Solutions to problems of this kind generally use a heuristic approach, which tries to find a sufficiently good (not necessarily optimal) solution in a reasonable time. We go through two stages in solving the timetabling problem. The first stage is to schedule all classes without breaking any predefined rules. The second stage optimizes the timetable generated in the first stage. This study attempts to solve the class timetabling problem issued in a competition called the 2019 International Timetabling Competition (ITC 2019). In the first stage, we use the Iterative Forward Search (IFS) algorithm to eliminate timetable candidates and to generate a schedule. In the second stage, we employ the Great Deluge algorithm with a hyper-heuristic approach to optimize the solution produced in the first stage. We have tested the method using 30 datasets by taking 1,000,000 iterations on each dataset. The result is an application that does schedule elimination and uses the IFS algorithm to produce a schedule that does not violate any of the hard constraints on 30 ITC 2019 datasets. The implementation of the Great Deluge algorithm optimizes existing schedules with an average penalty reduction of 42%.

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