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All Journal Semantik Jurnal Buana Informatika CommIT (Communication & Information Technology) Proceeding of the Electrical Engineering Computer Science and Informatics IJITEE (International Journal of Information Technology and Electrical Engineering) Jurnal Informatika Atma Jogja
Albertus Joko Santoso
Universitas Atma Jaya Yogyakarta
Computer Science & IT Electrical & Electronics Engineering Other

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Pengenalan Motif Sarung (Utan Maumere) Menggunakan Deteksi Tepi Imelda Dua Reja; Albertus Joko Santoso
Semantik Vol 3, No 1 (2013): Semantik 2013
Publisher : Semantik

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1341.806 KB)

Abstract

Seni budaya indonesia sangat beragam, diantaranya adalah budaya Tenun Ikat Sikka (Maumere  -  Flores  –  NTT) yangdigunakan sebagai salah satu bahan untuk melakukan upacara adat daerah setempat. Citra motif dari sarung (utan Maumere) ini akan digunakan sebagai bahan penelitian untuk mengenali motif dengan deteksi tepi. Pengolahan citra ini sangat dibutuhkan, terutama  dalam menganalisis tingkat keraguan dalam pengambilan keputusan dari suatu objek atau gambar. Dalam pengambilan keputusan, maka perlu menerapkan metode deteksi tepi. Dalam penelitian ini,  ada beberapa  metode deteksi tepi  yang  digunakan untuk melakukan beberapa analisis  sehingga dapat ditentukan operator deteksi tepi yang tepat untuk pengenalan motif sarung. Berdasarkan hal tersebut, penulis mencoba untuk menganalisis kinerja deteksi tepi antara lain deteksi tepi Operator Gradien Pertama menggunakan operator Sobel dan  Operator Canny serta  operator deteksi tepi dengan Operator Turunan Kedua menggunakan operator Laplace.Deteksi tepi yang dihasilkan dengan metode Canny merupakan deteksi tepi baik, karena garis yang dihasilkan dari deteksi tepi dengan metode Ca nny morfologinya sangat halus, dan semua garisnya terhubung. Dari  ketiga  metode tersebut dapat disimpulkan bahwa metode yang cocok untuk menentukan motif sarung (utan Maumere) adalah metode Canny.
Prediksi Kunjungan Wisatawan Taman Nasional Gunung Merbabu dengan Time Series Forecasting dan LSTM Josua Manullang; Albertus Joko Santoso; Andi Wahju Rahardjo Emanuel
Jurnal Buana Informatika Vol. 11 No. 2: Vol 11, No 2 (2020): Jurnal Buana Informatika Volume 11 - Nomor 2 - Okober 2020
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v11i2.3825

Abstract

Abstract. Prediction of tourist visits of Mount Merbabu National Park (TNGMb) needs to be done to control the number of visitors and to preserve the national park. The combination of time series forecasting (TSF) and deep learning methods has become a new alternative for prediction. This case study was conducted to implement several methods combination of TSF and Long-Short Term Memory (LSTM) to predict the visits. In this case study, there are 18 modelling scenarios as research objects to determine the best model by utilizing tourist visits data from 2013 to 2018. The results show that the model applying the lag time method can improve the model's ability to capture patterns on time series data. The error value is measured using the root mean square error (RMSE), with the smallest value of 3.7 in the LSTM architecture, using seven lags as a feature and one lag as a label.Keywords: Tourist Visit, Taman Nasional Gunung Merbabu, Prediction, Recurrent Neural Network, Long-Short Term MemoryAbstrak. Prediksi kunjungan wisatawan Taman Nasional Gunung Merbabu (TNGMb) perlu dilakukan untul pengendalian jumlah pengunjung dan menjaga kelestarian taman nasional. Gabungan metode antara time series forecasting (TSF) dan deep learning telah menjadi alternatif baru untuk melakukan prediksi. Studi kasus ini dilakukan untuk mengimplementasi gabungan dari beberapa macam metode antara TSF dan Long-Short Term Memory (LSTM) untuk memprediksi kunjungan pada TNGMb. Pada studi kasus ini, terdapat 18 skenario pemodelan sebagai objek penelitian untuk menentukan model terbaik, dengan memanfaatkan data jumlah kunjungan wisatawan di TNGMb mulai dari tahun 2013 sampai dengan tahun 2018. Hasil prediksi menunjukkan pemodelan dengan menerapkan metode lag time dapat meningkatakan kemampuan model untuk menangkap pola pada data deret waktu. Besar nilai kesalahan diukur menggunakan root mean square error (RMSE), dengan nilai terkecil sebesar 3,7 pada arsitektur LSTM, menggunakan tujuh lag sebagai feature dan satu lag sebagai label. Kata Kunci: Kunjungan Wisatawan, Taman Nasional Gunung Merbabu, Prediksi, Recurrent Neural Network, Long-Short Term Memory
Factors Affecting the Successful Implementation of E-Government on Network Documentation and Legal Information Website in Riau Muhammad Ikhsan Wibowo; Albertus Joko Santoso; Djoko Budiyanto Setyohadi
CommIT (Communication and Information Technology) Journal Vol. 12 No. 1 (2018): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v12i1.4361

Abstract

Network documentation and legal information website is a form of e-government applications such as Jaringan Dokumentasi dan Informasi Hukum (JDIH). JDIH website must be designed to be an effective and efficient information system. This study aims to determine the success factors of the implementation of JDIH website. The DeLone and McLean model and the Unified Theory Acceptance and Use of Technology (UTAUT) are the models used in this study. The case study is conducted in the Riau legal and human rights office. Data are obtained through questionnaires from 252 respondents in the Riau provincial government and some communities. The analysis uses Structural Equation Modeling (SEM) and Analysis of Moment Structure (AMOS). The results of this study show that nine hypotheses have positive effects. Meanwhile, four hypotheses have no positive effects on the success and use of JDIH website. The findings of this research will be used as a reference inthe development of JDIH website in the future.
Honey Yield Prediction Using Tsukamoto Fuzzy Inference System Tri Hastono; Albertus Joko Santoso; Pranowo Pranowo
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (398.026 KB) | DOI: 10.11591/eecsi.v4.1084

Abstract

Honey is a natural product of bee. Since ancient times, honey has been known by humans as a source of natural food and also for traditional medicine. There are so many beneficial of honey, make people trying to do honeybee cultivate as a business solution to increase their income. However, to cultivate honey bees is not easy. Special knowledge is required on honey bee cultivation and capital is fairly large. In order for beekeepers not to lose from honey sales business, beekeepers should be able to estimate the honey yield accurately. Predicted yield of honey is used as a material consideration and help determine the decision in honey bee cultivation. This study provides  a  solution  for  prediction  of  honey  yield  type  Apis Cerana with the main food of Calliandra flowers accurately. The method used in this research is Tsukamoto's fuzzy inference system (FIS) method. There are 3 input fuzzy used in this study, namely : Rainfall, number of box, and number of flower trees. The three fuzzy inputs are the determinants of the honey yield. The representation model used in the research is Trapezoid with fuzzy rules of 125 rules. While the test data in this research are rainfall and honey yield data for 21 years. The results of this study showed that the prediction of honey yield   using FIS Tsukamoto  closed  the  real  honey  yield  with  RMSE  value  of 9.44933860119277.
Prediction of Peat Forest Fires Using Wavelet and Backpropagation Novera Kristianti; Albertus Joko Santoso; Pranowo Pranowo
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 2, No 2 (2018): June 2018
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1289.705 KB) | DOI: 10.22146/ijitee.42156

Abstract

One of the causes of smog as well as climate damage, particularly in Palangka Raya, Center Kalimantan, are peat forest fires. There are a lot of losses inflicted by the smog including the increasing number of people who suffer respiratory infection (ARI) due to polluted air and any other related aspects. Peat fires are problematic to overcome because the locations of fires are difficult to be accessed. This paper focuses on building the system to predict the distribution of peat forest fire hotspots by utilizing satellite imagery. In designing the system for predicting the fire hotspots distribution, wavelet orthogonal was used as the initial processing of mapping the distribution of peat forest fire hotspots. Meanwhile, backpropagation method was used to identify the fire hotspot distribution patterns of peat forest fire in this system. From the result of the data tested which had been done for predicting the peat forest fire hotspots, the decomposition image obtained using Haar wavelet had the highest percentage of accuracy to recognize the fire hotspots, which is 90%. The recency of this system was its ability to predict the peat forest fire hotspots distribution which can be used as peat forest fires prevention, especially in Palangka Raya, Central Kalimantan.
Sistem Rekomendasi Objek Pariwisata di Pontianak Berbasis Android Menggunakan Metode Content-Based Filtering Kevin Christofer; Albertus Joko Santoso; Andi Wahju Rahardjo Emanuel
Jurnal Informatika Atma Jogja Vol. 1 No. 1 (2020): Jurnal Informatika Atma Jogja - November
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Kalimantan Barat merupakan provinsi terbesar ketiga di Indonesia. Sebagai Ibukota Provinsi, Pontianak juga memiliki banyak objek wisata, kuliner, rumah adat dan sebagainya. Banyaknya tempat wisata yang ditawarkan di Pontianak sendiri membuat wisatawan terkadang bingung untuk menentukan pilihan objek wisata mana yang akan dikunjungi. Sering juga ketika memutuskan untuk berkunjung ke sebuah tempat wisata, kadang wisatawan belum mengetahui apakah objek wisata yang hendak dikunjungi tersebut sesuai atau tidak dengan keinginannya. Oleh karena itu penulis akan membuat sistem rekomendasi objek wisata kota Pontianak. Sistem rekomendasi objek pariwisata ini bertujuan untuk membantu wisatawan untuk mendapatkan informasi objek-objek wisata yang berada di kota Pontianak dan sekitarnya. Dengan menggunakan metode Content-based Filtering, sistem akan melihat objek wisata yang wisatawan pilih sebelumnya dan memberikan rekomendasi objek wisata menggunakan metode tersebut. Agar nyaman penggunaan sistem saat berwisata, sistem ini dibangun untuk ponsel pintar bersistem operasi Android menggunakan Android Studio.Kata Kunci: Pariwisata, Sistem Rekomendasi, Pontianak, Content-based Filtering.
Co-Authors Andi Wahju Rahardjo Emanuel Djoko Budiyanto Setyohadi Imelda Dua Reja Josua Manullang Kevin Christofer Kristianti, Novera Muhammad Ikhsan Wibowo Pranowo Pranowo Tri Hastono