Baihaqi, Wiga Maulana
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THE IMPLEMENTATION OF SIMPLE ADDITIVE WEIGHTING METHOD IN THE SELECTION OF REHABILITATION FUND RECIPIENTS FOR UNINHABITABLE HOME Krisbiantoro, Dwi; Baihaqi, Wiga Maulana
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 10, No 1 (2019): JURNAL SIMETRIS VOLUME 10 NO 1 TAHUN 2019
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (444.401 KB) | DOI: 10.24176/simet.v10i1.3023

Abstract

The rehabilitation program is one of the programs given to people who have homes that are not habitable. They are usually from poor families with low economic income. In this program, the family will receive funds to rehabilitate their home. However, as long as the program is running, various problems have been encountered, including those who received fund sometimes received back the fund for rehabilitation funds. This is of course not in accordance with regulations that only allow applicants to receive the fund once. Based on this problem, a decision support system was made to select potential recipients of rehabilitation funds for uninhabitable house. By using the SAW method which is based on the value of criteria and preference weights, an appropriate assessment and ranking can be obtained after going through the selection process of assessing the weight of each attribute. The support for the selection decision for receiving uninhabitable rehabilitation fund was generated in this study. Decision making to determine beneficiaries was facilitated by the existence of a decision support system that was submitted, so that the fund provided was targeted at those entitled to receive uninhabitable rehabilitation fund.
REGRESI LINIER SEDERHANA UNTUK MEMPREDIKSI KUNJUNGAN PASIEN DI RUMAH SAKIT BERDASARKAN JENIS LAYANAN DAN UMUR PASIEN Baihaqi, Wiga Maulana; Dianingrum, Melia; Ramadhan, Kurnia Aswin Nuzul
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 10, No 2 (2019): JURNAL SIMETRIS VOLUME 10 NO 2 TAHUN 2019
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (898.491 KB) | DOI: 10.24176/simet.v10i2.3484

Abstract

Rumah Sakit merupakan sebuah institusi pelayanan kesehatan yang menyediakan dan memberikan pelayanan kesehatan kepada masyarakat. RSUD Cilacap merupakan Rumah Sakit Umum Daerah milik Kabupaten Cilacap yang merupakan Rumah Sakit terbesar di Daerah Cilacap. Seiring bertambahnya jumlah populasi manusia dan keadaan perekonomian yang semakin maju, maka tingkat kesadaran masyarakat terhadap kesehatan semakin meningkat. Maka diperlukan sebuah metode untuk memprediksi jumlah kunjungan pasien pada RSUD Cilacap. Perkiraan jumlah kunjungan pasien merupakan hal yang sangat penting bagi pihak Rumah Sakit, karena dapat digunakan untuk membantu pihak dari manajemen Rumah Sakit dalam melakukan sebuah perencanaan serta mengambil suatu kebijakan. Tujuan dari penelitian ini adalah untuk mengetahui hasil prediksi jumlah kunjungan pasien pada RSUD Cilacap menggunakan metode regresi linier. Metode regresi linier merupakan metode yang terdiri dari satu atau lebih variabel independen yang biasa dengan notasi X dan satu variabel respon yang bisa diwakili dengan Y. Pada penelitian ini Metode prediksi regresi linier dapat menghasilkan prediksi dengan beberapa kriteria nilai error MAPE, dimana terdapat 26 model prediksi regresi linier yang memiliki nilai error kurang dari 20% artinya mempunyai akurasi sebesar 80%. Akan tetapi, terdapat 3 model prediksi regresi linier yang masuk dalam kategori buruk yaitu nilai errornya lebih dari 50%, dan terdapat 1 model prediksi regresi linier yang termasuk dalam kategori cukup atau mempunyai nilai error sebesar 20% sampai 50%.
Penerapan Teknik Clustering Sebagai Strategi Pemasaran pada Penjualan Buku Di Tokopedia dan Shopee Baihaqi, Wiga Maulana; Indartono, Kuat; Banat, Syifaul
Paradigma - Jurnal Komputer dan Informatika Vol 21, No 2 (2019): Periode September 2019
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (487.82 KB) | DOI: 10.31294/p.v21i2.6149

Abstract

Pustaka Aysha is one of the online bookstores in Shopee and Tokopedia. Shopee and Tokopedia are online shopping sites that are top ranked in Indonesia. The amount of competition that exists between stores so that requires a marketing strategy. This research uses clustering techniques in data mining marketing strategies. Clustereing is one technique in data mining to find data sets that have similarities with other data or data dissimilarity with others. The clustering process is carried out using k-means and k-medoids on the sales transaction data of the Pustaka Aysha bookstore in Shopee and Tokopedia on March 2019 and consists of each of the 488 data divided into 3 clusters namely the first cluster for the most product in demand, the second cluster for products that are quite popular and the third cluster for products that are of little interest. Both of these algorithms will be clustered evaluation to find out which algorithm has better performance in this research, the evaluation process is carried out using davies bouldin index to maximize inter cluster distance and minimize intra cluster distance, so the results obtained that the k-medoids algorithm have performance better than k-means.
Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm Hariguna, Taqwa; Baihaqi, Wiga Maulana; Nurwanti, Aulia
International Journal of Informatics and Information Systems Vol 2, No 2: September 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i2.13

Abstract

In an e-commerce Shopee, the process of selling and buying continues to run every day, and the comments given by consumers will increase more and more. Comments given by consumers will be the reference/review of a product that has been purchased by consumers. Consumers freely provide a review containing positive comments and negative comments in the Comments field listed on the Shopee e-commerce website. With the above problems, researchers will do a research with the method of sentiment analysis to distinguish classes in product review comments that include positive comment class or negative comment class using a combination of K-means and naive Bayes classifier. K-means used to determine the grouping of classes; naive Bayes classifier used to get the value of accuracy. The results obtained based on clustering K-means include getting 116 negative comments on product reviews and 37 negative comments product reviews. Accuracy results obtained from product review comment data of 77.12%. Thus, the accuracy value using K-means and naive Bayes classifier without manual data get a higher accuracy value is compared using K-means, Naive Bayes classifier, and manual data get results lower accuracy of 56.86%. From the results above the most comments is a negative comment of 116 data review comments product, from the results of the study can be concluded that one of the products of Spatuafa named high heels women know the Ribbon Ikat FX18 the condition of the product is not good enough due to the high negative comments compared to positive comments