Article Per Year (5 Year)
p-Index From 2019 - 2024
1.922
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) JURNAL MEDIA INFORMATIKA BUDIDARMA International Journal of Computing Science and Applied Mathematics Charity : Jurnal Pengabdian Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer I-Com : Indonesian Community Journal eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT)
Gia Septiana Wulandari
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Public Health Social Sciences Other

Published : 17 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 17 Documents
Search

 1   2 
Optimasi Genetic Algorithm Dengan Simulated Annealing untuk Multiple Depot Capacitated Vehicle Routing Problem Aditya Permana; Mahmud Dwi Sulistyo; Gia Septiana Wulandari
Indonesia Symposium on Computing Indonesia Symposium on Computing 2015
Publisher : Indonesia Symposium on Computing

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Multiple Depot Capacitated Vehicle Routing Problem (MDCVRP) adalah pengembangan dari Vehicle Routing Problem (VRP). Tujuan objektifnya adalah mencari rute kendaraan dengan biaya termurah dari depot ke setiap pelanggan yang harus dikunjungi. Pada MDCVRP, permasalahan bertambah dengan bertambahnya jumlah depot dan adanya constraint tambahan berupa batas kapasitas kendaraan. Secara matematis, MDCVRP ini merupakan permasalahan kombinatorial yang termasuk ke dalam kategori NP- hard (non-deterministic polynomial-time hard). Pada penelitian ini, digunakan Genetic Algorithm (GA) yang dioptimasi oleh algoritma Simulated Annealing (SA) dalam menyelesaikan permasalahan MDCVRP. Pada dasarnya, GA sudah cukup bagus dalam menyelesaikan berbagai permasalahan kombinatorial. Namun, kekurangannya adalah adanya kemungkinan GA dalam proses pencariannya terjebak dalam kondisi optimal lokal. Untuk mengatasi kekurangan tersebut, SA hadir untuk mengoptimalkan performansi GA agar terhindar dari konvergensi prematur karena terjebak dalam optimum lokal sehingga hasil yang diperoleh menjadi lebih baik. Observasi telah dilakukan beberapa kali sehingga mendapatkan setting terbaik untuk beberapa parameter yang berpengaruh terhadap sistem. Dari hasil percobaan, terbukti bahwa penerapan SA untuk mengoptimaasi GA selalu dapat menaikkan performansi sistem sebesar sekitar 1-2%. Dari keseluruhan pengujian yang telah dilakukan, performansi terbaik yang dihasilkan oleh sistem mencapai 94.51%.  
MULTI-DEPOT VEHICLE ROUTING PROBLEM WITH TIME WINDOW MENGGUNAKAN ADAPTIVE GENETIC ALGORITHM DENGAN FUZZY LOGIC CONTROLLER Fazarudin, Tri Kusnandi; Dwi Sulistiyo, Mahmud; Wulandari, Gia Septiana
Jurnal Teknologi Vol 8 No 2 (2015): Jurnal Teknologi
Publisher : Jurnal Teknologi, Fakultas Teknologi Industri, Institut Sains & Teknologi AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Multi-Depot Vehicle Routing Problem with Time Window (MDVRPTW) adalah permasalahan pencarian rute optimal bagi suatu penyuplai. Penyuplai tersebut perlu mengirimkan barang ke sejumlah pelanggan dengan menggunakan kendaraan yang terdapat pada sejumlah depot. Setiap pengiriman barang tersebut harus dilakukan dalam rentang waktu pelayanan yang ditentukan oleh setiap pelanggan. Kendaraan yang digunakan mempunyai batasan jumlah maksimal barang yang dapat dibawa, dan waktu maksimal kendaraan tersebut boleh digunakan. MDVRPTW merupakan salah satu variasi dari Vehicle Routing Problem (VRP). Terdapat berbagai algoritma yang telah digunakan untuk menyelesaikan permasalahan VRP. Beberapa algoritma tersebut adalah Genetic Algorithm (GA), Tabu Search, dan Adaptive GA dengan Artificial Bee Colony. GA dapat menyelesaikan permasalahan dalam waktu yang lebih singkat, tetapi rentan terjebak dalam optimum lokal. Salah satu strategi untuk mengurangi kemungkinan terjadinya hal tersebut adalah dengan membuat GA menjadi adaptif. Pada penelitian ini, MDVRPTW diselesaikan dengan GA. Untuk mengurangi kemungkinan GA untuk terjebak pada optimum lokal, parameter pada GA dibuat menjadi adaptif menggunakan Fuzzy Logic Controller (FLC). Dari hasil penelitian yang sudah dilakukan, penggunaan FLC pada GA dapat meningkatkan rata-rata kualitas solusi yang dihasilkan lebih baik dibandingkan dengan GA yang tidak menggunakan FLC.
Prediction of Basic Material Prices on Major Holidays Using Multi-Layer Perceptron Ihsan, Rivan Nur; Saadah, Siti; Wulandari, Gia Septiana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3508

Abstract

The prediction of the price of basic necessities on major holidays in Indonesia, such as Eid al-Fitr, Christmas, New Year, Chinese New Year, and Eid al-Adha, is something that needs to be observed, because there are often movements in the prices of basic commodities that increase or decrease very drastically. One of the main ingredients experiencing this is eggs, which often experience a significant increase, so it is necessary to make observations in the form of predictions to keep control of fluctuations, especially before and after the big day occurs. In this study, predictions were made on the price of basic commodities on the big day. With the prediction of the cost of goods on the big day, it is hoped that related parties can be assisted in monitoring and stabilizing the movement of basic commodity prices on the market. In this study, a prediction system for the price of basic commodities was produced using the Multi-Layer Perceptron (MLP) method. This MLP method can predict time-series data that experiences a lot of fluctuation. In this prediction, MLP can make predictions on ten prices of basic commodities on major holidays every day. The results of this study were divided into three groups, namely Worst, Average, and Best. The division of these three groups separates which staple ingredients have the closest predictions to their actual values. The Worst group is the group whose prediction results are still quite far from the actual, the Average group which is close to the actual value, and the Best group which has the best results because it is very close to the actual value. With prediction results measured using MSE, the Worst group consisted of cooking oil (MSE 0.00197), beef (0.00186), rice (0.00118), and sugar (0.00100). Then the Average group consisted of eggs (0.00096), red chili (0.00085), chicken (0.00074), garlic (0.00062), and cayenne pepper (0.00056). Finally, the Best group only consisted of Shallots with an MSE of 0.00040.
Pengembangan Situs Web Sebagai Pembaharu Media Informasi Taman Kanak-Kanak Islam Terpadu (TKIT) Luqmanul Hakim Bandung Gia Septiana Wulandari; Febryanti Sthevanie; Mahmud Dwi Sulistiyo
I-Com: Indonesian Community Journal Vol 2 No 2 (2022): I-Com: Indonesian Community Journal (Agustus 2022)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (789.317 KB) | DOI: 10.33379/icom.v2i2.1505

Abstract

TKIT Luqmanul Hakim merupakan taman kanak-kanak di kota Bandung yang berada di bawah naungan Yayasan Pendidikan Luqmanul Hakim. Di masa pandemi yang melanda dunia sejak dua tahun lalu, kebutuhan akan informasi yang tersedia secara daring semakin meningkat, baik bagi pihak sekolah maupun orangtua. Kebutuhan tersebut dirasakan pula oleh orang tua calon siswa yang sedang mencari sekolah untuk anaknya, sebagai bahan pertimbangan pemilihan sekolah bagi anaknya. Akan tetapi, TKIT Luqmanul Hakim Bandung sampai sekitar pertengahan tahun 2022 masih belum memiliki situs web yang dapat diakses orang tua/wali siswa/calon siswa. Oleh karena itu, untuk memperluas persebaran informasi dari pihak sekolah, Tim Abdimas dari Fakultas Informatika, Universitas Telkom menerapkan metode hibah teknologi tepat guna dengan cara membangun sebuah situs web resmi untuk TKIT Luqmanul Hakim dengan menggunakan framework Laravel. Untuk keberlanjutan pengelolaan situs web ini, disediakan pula halaman untuk Administrator dan dilaksanakan pelatihan pengelolaan situs web untuk para guru TKIT Luqmanul Hakim. Kegiatan ini mendapat sambutan positif dari berbagai pihak dan sedang dirasakan manfaatnya secara nyata, serta terus diupayakan pengelolaannya sehingga informasi yang disajikan senantiasa aktual.
Solving Yin-Yang Puzzles Using Exhaustive Search and Prune-and-Search Algorithms Made Indrayana Putra; Muhammad Arzaki; Gia Septiana Wulandari
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol 8, No 2 (2022)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.v8i2.13720

Abstract

We investigate some algorithmic and mathematical aspects of Yin-Yang/Shiromaru-Kuromaru puzzles. Specifically, we discuss two algorithms for solving arbitrary Yin-Yang puzzles, namely the exhaustive search approach and the prune-and-search technique. We show that both algorithms have an identical asymptotic running time of O(max{mn, 2^(mn−h)}) for finding all solutions of a Yin-Yang instance with h hints of size m x n. Nevertheless, our experiments show that the practical running time of the prune-and-search technique outperforms the conventional exhaustive search approach.
Analysis of Multi-Layer Perceptron and Long Short-Term Memory on Predicting Cocoa Futures Price Abbsumarmanali Firyabi Sakhtiyani; Siti Saadah; Gia Septiana Wulandari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4498

Abstract

Predicting the price of Cocoa Futures is needed by farmers and also the government in determining policies. The uncertainty of price movements can affect farmers’ income and also foreign exchange savings because Indonesia is the largest cocoa-producing country in the world. In this study, we use the cocoa futures dataset to train using Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) to make a prediction of the cocoa futures price. In that way, this study resolves the uncertainties using the MLP method and also the LSTM, where these two methods produce a model using the input of data train and data test to predict the price of cocoa futures contracts and then be compared to see which one is the right one for the cocoa dataset. The dataset used is quoted from the Investing.com page taken from 2003 to 2021. The result of this study is the best model between MLP and LSTM model, where the LSTM can produce the best model using 50-50 Train to test data ratio, 128 batch size, and 64 Neurons on the hidden layer with evaluation metrics value in RMSE is 2.27, MAE is 32.11, and MAPE is 1.29 or 98.71% accuracy. This is because the LSTM model has logic gates in the layers that have an advantage on time series data using memory, where the LSTM model could memorize the output and use the output again as an input to achieve the best output.
Export Commodity Price Forecasting in Indonesia Using Decision Tree, Random Forest, and Long Short-Term Memory Shadifa Auliatama Harjanto; Siti Sa'adah; Gia Septiana Wulandari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.25242

Abstract

Gross Domestic Product (GDP) is an indicator that becomes a benchmark for a country's economic performance. One of the factors that significantly affect GDP is export activity. However, the problem that occurs is that the export value is relatively fluctuating, this is because commodity prices are always changing every time. Therefore, we need a system that can predict commodity prices accurately. It is hoped that this system can help the government to make appropriate export policies based on predictions of commodity prices in the future. The contribution of this study is to compare Decision Tree, Random Forest, and Long Short-Term Memory (LSTM) performance in forecasting several export commodities in Indonesia. In this study, the commodities forecasted are the main commodities from each sector that dominates exports in Indonesia, namely palm oil from the manufacturing sector, coffee from the agricultural sector, and coal from the mining sector. The experiments in this study were conducted by testing several hyperparameters of each method to determine the best model. The performance of models is measured using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that LSTM has the lowest error among Decision Tree and Random Forest with MAPE of 0.121, 0.494, and 0.282 in forecasting coal, coffee, and palm oil price respectively. Therefore, LSTM has proven to be the best method among Random Forest and Decision Tree in forecasting export commodity prices in Indonesia.
Solving Tatamibari Puzzle Using Exhaustive Search Approach Enrico Christopher Reinhard; Muhammad Arzaki; Gia Septiana Wulandari
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 3 (2022): December, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.3.675

Abstract

Tatamibari is a puzzle that was first published in 2004 and was proven to be NP-complete in 2020. However, to the best of our knowledge, algorithmic investigation of the Tatamibari puzzle is relatively new and limited. There are discussions about an approach for solving the Tatamibari puzzle using the Z3 SMT solver, but there are no details regarding the steps of the algorithm as well as its explicit asymptotic upper bound. In addition, this solver requires an additional library that cannot be directly executed using standard libraries in an arbitrary imperative programming language. Hence, this paper discusses an exhaustive search approach for solving an arbitrary Tatamibari puzzle. We show that this algorithm can find all solutions to an \(m \times n\) Tatamibari instance with \(h\) hints in \(O(\max\{m^2 n^2, h^{mn-h} \cdot hmn\})\) time. We also use this algorithm to find the number of possible Tatamibari solutions in an \(m \times n\) grid for some small values of \(m\) and \(n\).
Prediksi Struktur Sekunde r RNA Mengg una ka n Stochastic Context Free Grammar dan Gra mmatical Evolution Asriyanti Indah Pratiwi; Agung Toto Wibowo; Gia Septiana Wulandari
eProceedings of Engineering Vol 2, No 1 (2015): April, 2015
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Salah s atu metode prediks i s truktur s ekunder RNA (ribonucleic acid) adalah SCFG (stochastic context free grammar) . Na mun SCFG me miliki ketegantungan yang tinggi terhadap grammar. Gra mmar yang kurang baik a kan berdampak buruk terhadap performans i prediks i. Ha l ini menyebabkan has il prediks i menjadi t idak optima l. Oleh karena itu, penelitian in i berfokus dala m perancangan probabilitas s etiap production rules dari grammar untuk men ingkatkan nilai sensitivity dari grammar Wats on Crick yang biasa digunakan dalam SCFG. Untuk mencapai nilai sensitivity yang lebih baik ma ka da la m penelitian in i dibangun s ebuah s istem menggunakan grammatical evolution untuk mendapatkan probabilitas s etiap production rule dari grammar. Penelitian ini berhas il meningkat kan nilai sensitivity grammar. Da ri has il pengujian d idapatkan nilai sensitivity s ebes ar 0,32-0,42. Keywords: prediksi, struktur sek under R NA, SCF G, Grammatical Evolution , nilai sensitivity
Analisis Performansi Metode Graph Decomposition Index pada Graph Database Isjhar Kautsar; Kemas Rahmat Saleh Wirayuda; Gia Septiana Wulandari
eProceedings of Engineering Vol 2, No 1 (2015): April, 2015
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kekurangan relational database yang ditemui, seperti sulitnya membuat desain relational database yang pas, kurang mampu mengakomodir data semi terstruktur, dan kurang mampunya mengakomodir data yang memiliki banyak relasi mendorong para peneliti untuk menemukan model database yang baru. Salah satunya graph database. Graph database mampu menjadi solusi berbagai permasalah tersebut. Namun, graph database sendiri masih memiliki beberapa kekurangan, yaitu pencarian dilakukan secara sekuensial pada saat proses retrieve data. Oleh karena itu, suatu metode diperlukan untuk mengatasi masalah ini, yaitu dengan indexing. Metode indexing yang akan digunakan adalah graph decomposition index. Graph decomposition index cocok dengan jenis data yang digunakan, yaitu pada bagian keunggulannya dalam me-retrieve data query berupa subgraph dan model graph berupa simpel graph berlabel. Penelitian ini akan membangun dua buah sistem yaitu graph database tanpa index dan graph database dengan index. Kemudian dibandingkan performansinya berdasarkan response time (execution time dan IO time). Dari penelitian ini diperoleh hasil bahwa graph database dengan index akan memiliki performansi execution time yang lebih bagus dibanding graph database tanpa index, tetapi memiliki IO time yang jelek dibanding graph database tanpa index Keywords—graph database; indexing; relational; graph decomposition index.
Co-Authors Abbsumarmanali Firyabi Sakhtiyani Aditya Permana Agung Toto Wibowo Asriyanti Indah Pratiwi Enrico Christopher Reinhard Febryanti Sthevanie Ihsan, Rivan Nur Isjhar Kautsar Kemas Rahmat Saleh Wiharja Kiki Dwi Prasetyo M Haikal Abdussalam Made Indrayana Putra Mahmud Dwi Sulistiyo Mahmud Dwi Sulistyo Mahmud Dwi Sulistyo Muhammad Arzaki Nungki Selviandro Pratama Juliantono Taufiq Rifki Wijaya Ryan Adeputra Sutopo Shadifa Auliatama Harjanto Siti Sa'adah Siti Saadah Siti Saadah Tri Kusnandi Fazarudin, Tri Kusnandi Vincentius Arnold Fridolin