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Pengembangan Sistem Arsip Dokumen Keuangan (SIMARKU) Sekretariat DPRD Kabupaten Gianyar Budiastawa, I Dewa Gede
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 7 No 2 (2018): JELIKU Volume 7 No 2, 2018
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

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

Sekretariat DPRD Kabupaten Gianyar merupakan sebuah Lembaga yang memiliki tugas dalam mendukung tugas dan fungi DPRD. Sekretariat DPRD memberikan pelayanan administrasi dan fasilitasi untuk mewujudkan pelayanan prima kepada Lembaga Dewan Perwakilan Rakyat Daerah, sehingga segala macam kelengkapan administrasi DPRD akan dilayani oleh Sekretariat DPRD dengan demikian aka nada banyak berkas maupun dokumen yang menjadi arsip sebagai laporan kepada daerah termasuk arsip dokumen keuangan, selain sebagai kelengkapan pemeriksaan, dokumen keuangan juga menjadi acuan bendahara dalam melakukan pertanggungjawaban sehingga dana yang diberikan pemerintah daerah setiap bulannya dapat dikelola dan dipertanggung jawabkan. Dalam menjalankan tugasnya khususnya pada bagian keuangan, tak jarang terjadi kekeliruan dalam melakukan penginputan data dalam perhitungan jumlah uang yang telah terpakai sehingga akan dilakukan pencarian berkas kembali. Dari hasil Analisa, diidentifikasi bahwa Sekretariat DPRD Kabupaten Gianyar memerlukan sebuah system yang mampu memberikan informasi seputar arsip-arsip keuangan baik itu kegiatan-kegiatan maupun kunjungan kerja sehingga dapat memudahkan dalam melakukan pengecekan berkas kembali mupun pemeriksaan, dengan itu dibangun sebuah system informasi arsip dokumen keuangan. System informasi ini dibangun menggunakan beberapa Bahasa pemrograman diantaranya HTML, PHP, Javascript dan juga digunakan MySQL sebagai penyimpanan data.

PREDIKSI DAN AKURASI NILAI TUKAR MATA UANG RUPIAH TERHADAP US DOLAR MENGGUNAKAN RADIAL BASIS FUNCTION NEURAL NETWORK Budiastawa, I Dewa Gede; Santiyasa, I Wayan
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 7 No 4 (2019): JELIKU Volume 7 No 4, Mei 2019
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2019.v07.i04.p12

Currency exchange rates, or often referred to as the exchange rate, are the price of one unit of a foreign currency in a domestic currency or can also be called the price of a domestic currency against a foreign currency. The value of a country's currency is strongly influenced by the flow of capital between countries. The high exchange rate of other countries' currencies against a country will result in the deterioration of the economic situation of a country. The weakening of the currency exchange rate will cause Indonesia's foreign debt to increase and the balance sheets of companies and banks will decline. The phenomenon of volatile rupiah exchange rate fluctuations often occurs in Indonesia which will cause economic conditions, especially trade will be disrupted because trade is valued in US dollars (USD). Therefore, serious handling is needed in the face of erratic exchange rate fluctuations because it will affect the economic performance of a country so that a decision can be made after knowing the exchange rate of the next period. In helping to make decisions, the authors make a forecasting model of the rupiah exchange rate against USD using the radial basis function of the neural network. In this research used factors that influence the fluctuation of the rupiah exchange rate against IDR, namely the value of exports, imports, GDP, BI interest rates, inflation rates, and the money supply. In this research optimization of learning rate and hidden neuron parameters was done to get the lowest error value or error rate. The results of the research using the radial basis of neural network functions produce accuracy values ranging from 89 - 95% in the training process while the testing process ranges from 67 - 98% and with an error rate of 4 - 11% in the training process while 2 - 32% for testing process. Key Words : Exchange rates, forecasting, RBF, training, testing, accuracy

Comparison of the Sentiment Analysis Model's Code Complexity and Processing Time: Sentiment analysis for tolerance and religious moderation in Indonesia: A Case Study Kartika, Luh Gede Surya; Utama, Putu Kussa Laksana; Budiastawa, I Dewa Gede; Rinartha, Komang
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11894

Text processing, which includes sentiment analysis, obviously demands a lot of resources. The extent to which resources are used to promote environmental conservation is directly impacted by code complexity or computational time. Twitter users' responses on the Indonesian Language of religious moderation and tolerance are used to test the model. The phases of doing the research include determining the research's needs, gathering data, text preprocessing (case folding, tokenizing, stopword removal, and stemming), word weighting with TF/IDF, and classification with Multinomial Naive Bayes (MNB) and Support Vector Machine (SVM), validation, and the last step was calculation of the code complexity and computation time. The validation results showed that the performance of the two models was still low, with an average accuracy of 75.5%. Based on computation time, SVM has a faster computational time than MNB. However, when compared in terms of the code complexity, the Cyclomatic Complexity in both models was the same because both models used existing libraries in Python Interpreter, and the complexity of the libraries cannot be calculated directly. Based on the Raw Metrics, it can be seen that MNB and SVM not significantly different in LOC, LLOC, and SLOC. It was evident that SVM has a greater Halstead Complexity than SVM in all measures when comparing the program code of MNB and SVM. Particularly on programming effort and volume metrics, SVM required more bits to run than an MNB program, and it also required more mental effort to convert a prepared SVM algorithm into a program than an MNB.

Optimalisasi Metode RBFNN Dengan Fuzzy C-Means Dalam Prediksi Import Barang Konsumsi Indonesia Budiastawa, I Dewa Gede; Sunarya, I Made Gede; Wirawan, I Made Agus
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8711

Prediction or forecasting is an action that aims to find out future events based on indicators that influence an event. Consumer goods are products or goods purchased by people or households that are intended for direct consumption in the sense that they are not for further production purposes. Based on this, serious handling is needed to maintain the state of the Indonesian economy, especially in the industrial sector. Predicting the value of consumer goods imports is a step in finding out the value of consumer goods imports in the next period so that the government has a reference in determining policies. In this study, the prediction of the value of consumer goods imports was carried out based on factors that influence the value of consumer goods imports based on research in the field of economics. This study uses the Radial Basis Function Neural Network (RBFNN) method using a combination of clustering methods, namely Fuzzy C-Means Clustering to improve method performance. The RBFNN method is the best method used in predicting future data based on previous research and the FCM method is a clustering method that is able to overcome ambiguity in the prediction process. This study proves that the Fuzzy C-Means method is effective in optimizing the performance of the Radial Basis Function Neural Network method with a comparison of MAPE values in each combination, namely RBFNN - FCM 15.73%, RBFNN - K-Means 16.87% and RBFNN - Random centroid 17.70%. The learning rate parameter is directly proportional to the RBFNN - FCM model where the greater the learning rate, the better the model performance, indicating that the model does not need to do in-depth learning to recognize data patterns. In contrast to the fuzzification parameter which increases accuracy when the fuzzification value is lowered, indicating that the model does not require a very vague approach to recognize data patterns. The best architecture is 8 - 4 - 1 with a fuzzification parameter value of 1.5, a learning rate of 0.3 and a threshold error of 0.3 produced by a combination of RBFNN and FCM.

Optimalisasi Metode RBFNN Dengan Fuzzy C-Means Dalam Prediksi Import Barang Konsumsi Indonesia Budiastawa, I Dewa Gede; Sunarya, I Made Gede; Wirawan, I Made Agus
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8711

Prediction or forecasting is an action that aims to find out future events based on indicators that influence an event. Consumer goods are products or goods purchased by people or households that are intended for direct consumption in the sense that they are not for further production purposes. Based on this, serious handling is needed to maintain the state of the Indonesian economy, especially in the industrial sector. Predicting the value of consumer goods imports is a step in finding out the value of consumer goods imports in the next period so that the government has a reference in determining policies. In this study, the prediction of the value of consumer goods imports was carried out based on factors that influence the value of consumer goods imports based on research in the field of economics. This study uses the Radial Basis Function Neural Network (RBFNN) method using a combination of clustering methods, namely Fuzzy C-Means Clustering to improve method performance. The RBFNN method is the best method used in predicting future data based on previous research and the FCM method is a clustering method that is able to overcome ambiguity in the prediction process. This study proves that the Fuzzy C-Means method is effective in optimizing the performance of the Radial Basis Function Neural Network method with a comparison of MAPE values in each combination, namely RBFNN - FCM 15.73%, RBFNN - K-Means 16.87% and RBFNN - Random centroid 17.70%. The learning rate parameter is directly proportional to the RBFNN - FCM model where the greater the learning rate, the better the model performance, indicating that the model does not need to do in-depth learning to recognize data patterns. In contrast to the fuzzification parameter which increases accuracy when the fuzzification value is lowered, indicating that the model does not require a very vague approach to recognize data patterns. The best architecture is 8 - 4 - 1 with a fuzzification parameter value of 1.5, a learning rate of 0.3 and a threshold error of 0.3 produced by a combination of RBFNN and FCM.

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