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All Journal Jurnal Sains dan Teknologi Sinkron : Jurnal dan Penelitian Teknik Informatika SINTECH (Science and Information Technology) Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Mantik Journal of Intelligent Decision Support System (IDSS) ANALISIS - SCIENTIFIC JOURNAL OF ECONOMICS, MANAGEMENT, BUSINESS, AND ACCOUNTING Indonesian Journal of Data and Science Journal of Computer Networks, Architecture and High Performance Computing Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Krisnadana Jurnal Krisnadana Aspirasi : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat KOMET
Atmaja, Ketut Jaya
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Journal : Jurnal Mantik

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FP-Growth Implementation in Frequent Itemset Mining for Consumer Shopping Pattern Analysis Application I Made Dwi Putra Asana; I Komang Arya Ganda Wiguna; Ketut Jaya Atmaja; I Putu Anjas Sanjaya
Jurnal Mantik Vol. 4 No. 3 (2020): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.1075.pp2063-2070

Abstract

Most retail companies have implemented computer-based information systems for recording sales transaction data. In the implementation of information systems, the data collected in the database is processed limited to making reports such as sales reports and inventory reports. Database generated from computer-based information systems can be further processed to obtain more valuable information. One strategy for using sales transaction data is to analyze consumer spending patterns. Consumer spending patterns can be in the form of associations of items that are often purchased simultaneously. The association between goods can be determined using the frequent itemset search technique. The Fp-growth algorithm is an algorithm that can be used to determine frequent itemsets in a data set. This article describes the results of implementing the FP-Growth algorithm in the consumer shopping pattern analysis application. The resulting shopping pattern is in the form of goods that are often purchased simultaneously by consumers. From the results of the application of the fp-growth algorithm, it was found that the minimum value of support had an effect, namely the smaller the input value of support, the more pairs of items were obtained. The application of the FP-Growth algorithm in determining frequent itemsets in association data mining can find customer spending habits in buying goods simultaneously.
Sales Forecasting System Using Single Exponential Smoothing Ketut Jaya Atmaja; Ida Bagus Gede Anandita
Jurnal Mantik Vol. 4 No. 4 (2021): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2021.1207.pp2552-2557

Abstract

In a trading business, meeting customer demand is very important to do. Fulfilling customer demand can be done with good stock inventory management. Accuracy in carrying out stock management is important to maintain the level of satisfaction of consumers because of the needs being met. In addition, accuracy in carrying out stock management can affect the financial cash flow of a trading business. Over-stocking, over time it will become dead-stock because the goods being sold become obsolete, changes in market tastes, not to mention merchandise that has an expiration date. Meanwhile, too little stock can cause lost of sales because the level of demand from consumers is greater than the amount of existing stock. Forecasting systems can help maximize stock inventory management in meeting customer demand needs. Forecasting is an activity in predicting and predicting something that will happen in the future. Forecasting is done through calculation analysis techniques based on past data references. This data can be in the form of qualitative data and quantitative data. The exponential smoothing method is a forecasting method based on qualitative data from a time series of previous sales trends to predict the future. This method is best used to analyze fluctuating sales trends. To determine the accuracy of forecasting, the results of the forecasting are then analyzed using the MSE and MAPE methods.
Sales Forecasting Applications For Retail Companies Using Double Exponential Smoothing And Golden Section Methods I Made Dwi Putra Asana; I Made Deni Kurniadi; Sugihya Artha Dwipayani; Ketut Jaya Atmaja
Jurnal Mantik Vol. 6 No. 2 (2022): August: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v6i2.2584

Abstract

The availability of goods becomes one of the keys to the success of supermarket business in fulfilling consumer needs. The Forecasting methods can help to predict the sales in the future and it can help to find sales statistics daily, monthly or yearly. The application of exponential smoothing requires the process of determining the smoothing value by performing several tests. The determination of smoothing is a challenge in the forecasting process because it takes several tests of the optimal smoothing value to reduce forecasting errors. This study proposed the application of the golden section in optimizing the determination of the smoothing value. Golden section is an optimization method that provides extreme values ??of a non-linear function by reducing the range of values ??that contain extreme values. The results of the Forecasting method were based on the training data in which the trend and the result of the forecasting approached to the training data that used for forecasting. According to the results of the forecasting which conducted based on the training data was MAPE 26.460474 % and MAPE results from comparison of testing data obtained was 21.89696%.
Co-Authors Anandita, Ida Bagus Gede Asana, I Made Dwi Putra Ceryna Dewi, Ni Kadek Dirgayusari, Ayu Manik Ekayana, Anak Agung Gede Gunawan, I Komang Agus Bryan Hugo, Veronika Novia I Dewa Made Krishna Muku I Gede Adnyana I Gede Iwan Sudipa I Gusti Agung Indrawan I Kadek Agus Bisena I Kadek Dwi Gandika Supartha I Komang Arya Ganda Wiguna I Made Deni Kurniadi I Made Dwi Putra Asana I Made Dwi Putra Asana I Made Oka Widyantara I Nyoman Saputra Wahyu Wijaya I Nyoman Tri Anindia Putra I Putu Anjas Sanjaya I Putu Noven Hartawan I Putu Yoga Indrawan I Wayan Dharma Suryawan I Wayan Krishna Sangging Wiguna Ida Bagus Nyoman Pascima Indra Pratistha Ketut Sepdyana Kartini Libraeni, Luh Gede Bevi Meinarni, Ni Putu Suci Melyawati, Ni Luh Putu Ni Kade Ayu Nirwana Ni Kadek Ceryna Dewi Ni Komang Ayu Suandaniasih Ni Luh Wiwik Sri Rahayu Ginantra Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Ni Putu Juliana Dewi Ni Putu Sri Widiani Ni Putu Widantari Suandana Ni Wayan Suardiati Putri Nirwana, Ni Kade Ayu Prabhawa , I Kadek Angga Surya Pramita, Dewa Ayu Kadek Putra, I Putu Satria Udyana Putu Ananda Sitarasmi Putu Gede Surya Cipta Nugraha Putu Praba Santika Putu Wirayudi Aditama Radhitya, Made Leo Sandhiyasa, I Made Subrata Sandika, I Kadek Budi Sarasvananda , Ida Bagus Gde Saraswati, Ni Wayan Sumartini Sugihya Artha Dwipayani Udayanie, Ayu Gde Chrisna Wawan, I Kadek Wiguna, I Kadek Artha Wiguna, I Made Surya Wijaya, Bagus Kusuma Willdahlia, Ayu Gede