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All Journal Jurnal Manajemen Bisnis Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Applied Information System and Management J-MAS (Jurnal Manajemen dan Sains) Jurnal Soshum Insentif Jurnal Doktor Manajemen (JDM) JISA (Jurnal Informatika dan Sains) Mabny : Journal of Sharia Management and Business Journal of Economic, Business and Engineering (JEBE) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan SIGMA: Information Technology Journal Jurnal Ekonomika dan Manajemen Journal of International Conference Proceedings Jurnal Pengabdian Pelitabangsa Jurnal Lentera Pengabdian Joong-Ki Journal of Community Empowerment JEPEmas: Jurnal Pengabdian Masyarakat (Bidang Ekonomi) Strata International Journal of Social Issues Multidiscience: Journal of Multidisciplinary Science Joong-Ki
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Sentimen Analisis Data Twitter Terhadap Calon Wakil Presiden 2019 Sandiaga Salahuddin Uno Yoga Religia; Heri Purwanto
Jurnal SIGMA Vol 9 No 3 (2019): Maret 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (405.903 KB)

Abstract

Abstraksi Media social twitter merupakan salah satu contoh media social yang digunakan masyarakat untuk saling berinteraksi satu sama lain. Twitter memberikan layanan kepada penggunanya untuk mengirim dan membaca tweets yang telah dibagikan,sehinggam masyarakat lebih memilih menuangkan opininya melalui media social dari pada menyampaikannya secara langsung .Opini masyarakat yang tertuang dalam media social twitter berupa sebuah persepsi,baik itu positif maupun negative. Melimpahnya opini masyarakat dapat dimanfaatkan sebagai bahan penelitian untuk mencari sebuah informasi .Pemanfaatan informasi tersebut membutuhkan teknik analisis yang tepat sehingga informasi yang dihasilkan mampu membantu banyak pihak dalam mengambil keputusan .Penggunaan teknik dalam pengolahan data dapat diselesaikan teknik analisa sentiment atau opinion mining.Oleh sebab itu ,pada penelitian ini mencoba melakukan analisa sentiment untuk melihat persepsi masyarakat terhadap calon wakil presiden 2019 sandiaga salahudin unno dari partai gerindra pada media social twitter menggunakan metode Naïve Bayes Classifer dengan mengklasifikasikan sentimen menjadi positif,negative dan netral. Kata Kunci: Analisa Sentiment,Naïve Bayes Classifier,Persepsi,Twitter
Sistem Pendukung Keputusan Untuk Penilaian Kinerja Karyawan, Menggunakan Metode (Technique For Others Reference By Similarity To Ideal Solution ) Pada Pt.Indofarma (Persero) Tbk. Yoga Religia; Triyono Triyono
Jurnal SIGMA Vol 9 No 1 (2018): September 2018
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

Abstraksi Penelitian ini untuk menganalisis kinerja karyawan sehingga dapat memperoleh karyawan terbaik hingga terburuk untuk pengangkatan karyawan pada PT. Indofarma (Persero) Tbk.Sistem Penilaian kinerja perusahaan tersebut dilakukan analisis dari aspek proses penilaian kinerja, metode penilaian kinerja, dan melakukan evaluasi sistem penilaian kinerja berdasarkan syarat-syarat penilaian kinerja agar tercipta suatu sistem yang efektif, efisien dan mencerminkan kinerja aktual perusahaan. Penelitian ini mengimplementasikan metode TOPSIS untuk mengembangkan aplikasi pendukung keputusan untuk seleksi penerimaan tenaga kerja. TOPSIS dipilih karena memiliki konsep bahwa alternatif terpilih adalah alternatif yang memiliki jarak terpendek dengan solusi ideal positif dan memiliki jarak terjauh dengan solusi ideal negative. Hasil dari penelitian ini adalah sebuah sistem pendukung keputusan yang dapat menunjukkan hasil perangkingan dengan metode TOPSIS untuk proses Seleksi karyawan terbaik. Pengujian akurasi pada penelitian ini menggunakan rumus eucledian distance untuk membandingkan hasil perangkingan dalam proses seleksi. Hasil pengujian tersebut menunjukkan bahwa perangkingan dengan metode TOPSIS dan perangkingan pihak PT. Indofarma (Persero) Tbk memiliki nilai kemiripan 0.648, sehingga hasil seleksi dengan metode TOPSIS pada sistem pendukung keputusan ini bisa diterima. Melalui aplikasi Sistem Pendukung Keputusan Penilaian Kinerja Karyawan Menggunakan Metode TOPSIS pada PT. Indofarma berbasis web ini Akurat dalam pengambilan keputusan penilaian terbaik. mempermudah dan mempercepat pengolahan data serta pengambilan keputusan penilaian kinerja karyawan pada PT Indofarma yang baik dan objektif. Kata Kunci : Sistem Pendukung Keputusan, Penilaian Kinerja,Metode Topsis
SISTEM PAKAR PENDETEKSI KERUSAKAN MESIN PACKING OTOMATIS MODEL CP730B DENGAN METODE CASE BASED REASONING Yoga Religia; Erni Ratnasari2
Jurnal SIGMA Vol 10 No 4 (2019): Desember 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (476.229 KB) | DOI: 10.37366/sigma.v10i1.479

Abstract

In this study the problem was formulated about the proccess how to implement the Decision Support System using the Case Based Reasoning Method to detect damage to an automatic CP730B packing machine. While the purpose of this research is to design a system that can be used to detect damage to machines that often occur, find solutions to make it easier for technicians and production employees who use the CP730B model automatic packing machine to save time, effort and how to minimize the expense of maintenance costs. This system is made using PHP, for its Database using MySql Database and the method used in making this system is the Case Based Reasoning method. His system pupose provides knowledge of the symptoms of damage to the machine as well as solitions to how to deal with damage so as to reduce the maintenace cost budget by specialized technicians because almost all of the handling can be done alone using this system. The result of this system is to assist in the detection of breakages or problems that occur in an automatic packing machine. Keywords : Expert System, Case Base Reasoning, Php, Mysql Database.
SISTEM INFORMASI INVENTORY BARANG BERBASIS WEB DENGAN MENGGUNAKAN METODE WATERFALL PADA PT. MUSASHI AUTO PARTS INDONESIA Yoga Religia; Heriyanto2 Heriyanto2
Jurnal SIGMA Vol 10 No 4 (2019): Desember 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

Information systems are systems that process and provide information for decision making in an organization. Inventory information system is a system used to process and provide information about inventory data in a company for decision making. Goods Inventory Information System at PT. MUSASHI AUTO PARTS INDONESIA still uses manual processes. The purpose of this study is to develop an ongoing inventory information system at PT. MUSASHI AUTO PARTS INDONESIA to support web-based warehouse inventory control. The methodelogy used to develop this inventory information system is the waterfall method and uses the PHP and MySQL programming languages. Inventory information systems can overcome problems related to the quality of information and the clarity of information produced. Admin as user of inventory information system concludes that this system can facilitate the processing, searching, and reporting of data in and out of warehouse goods. Keywords: Information Systems, Inventory, Waterfall, PHP, MYSQL.
Penerapan Algoritma K-Means Dalam Pengelompokan Curah Hujan Di Daerah Jabodetabek Yoga Religia; Ari Susanto
Jurnal SIGMA Vol 8 No 2 (2018): Maret 2018
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

Abstrak Akhir-akhir ini cuaca semakin sulit untuk diprediksi, bahkan terkadang terjadi hujan saat musim kemarau dan sebaliknya. Hal ini sangat berpengaruh pada aktifitas sehari-hari seperti keselamatan masyarakat, sosial ekonomi, produksi pertanian, perkebunan, perikanan, penerbangan, dan sebagainya dalam suatu daerah. Maka dari itu informasi yang akurat tentang kondisi cuaca sangatlah penting sehingga kita dapat memperiapkan diri. Dalam penyampaian informasi agar lebih akurat perlu adanya penelitian tentang cuaca atau curah hujan. Dalam penelitian ini kita akan mengambil contoh kondisi cuaca di wilayah Jakarta dan sekitarnya dimana wilayah tersebut lebih beragam aktivitasnya. Algoritma K-Means adalah metode yang dapat digunakan dalam penelitian ini, yaitu untuk melakukan pengelompokan curah hujan. Data yang diambil adalah data dari BMKG untuk wilayah Jakarta dan sekitarnya dimana nantinya akan dikelompokan menjadi dua kelompok yaitu curah hujan tinggi dan rendah menggunakan algoritma K-Means dan metode perhitungan jarak Euclidean Distance. Pengelompokan curah hujan ini nantinya dapat memberikan informasi yang akurat sehingga dapat mengurangi dampak perubahan kondisi cuaca yang secara mendadak. Kata Kunci : Algoritma K-Means, Euclidean Distance, Pengelompokan Cuaca, Data Mining
Grouping of Village Status in West Java Province Using the Manhattan, Euclidean and Chebyshev Methods on the K-Mean Algorithm Gatot Tri Pranoto; Wahyu Hadikristanto; Yoga Religia
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1097

Abstract

The Ministry of Villages, Development of Disadvantaged Areas and Transmigration (Ministry of Village PDTT) is a ministry within the Indonesian Government in charge of rural and rural development, empowerment of rural communities, accelerated development of disadvantaged areas, and transmigration. Village Potential Data for 2014 (Podes 2014) in West Java Province is data issued by the Central Statistics Agency in collaboration with the Ministry of Village PDTT which is in unsupervised data format, consists of 5319 village data. The Podes 2014 data in West Java Province were made based on the level of village development (village specific) in Indonesia, by making the village as the unit of analysis. Base on the Regulation of the Minister of Villages, Disadvantaged Areas and Transmigration of the Republic of Indonesia number 2 of 2016 concerning the village development index, the Village is classified into 5 village status, namely Very Disadvantaged Village, Disadvantaged Village, Developing Village, Advanced Village and Independent Village based on the ability to manage and increase the potential of social, economic and ecological resources. Village status is in fact inseparable from village development that is under government funding support. However, village development funds have not been distributed effectively and accurately according to the conditions and potential of the village due to the lack of clear information about the status of the village. Therefore, the information regarding the villages priority in term of which villages needs more funding and attention from the government is still lacking. Data mining is a method that can be used to group objects in a data into classes that have the same criteria (clustering). One of the algorithms that can be used for the clustering process is the k-means algorithm. Data grouping using k-means is done by calculating the closest distance from data to a centroid point. In this study, different types of distance calculation in the K-means algorithm are compared. Those types are Manhattan, Euclidean and Chebyshev. Validation tests have been carried out using the execution time and Davies Bouldin index. From this test, the data Village Potential 2014 in West Java province have grouped all the 5 status of the village with the obtained number of villages for each cluster is a cluster village Extremely Backward many as 694 villages, cluster Villages 567 villages, cluster village Evolving as much as 1440 villages, the cluster with Desa Maju1557 villages and the cluster Independent Village for 1061 villages. For distance calculation, Chebyshev has the most efficient accumulation time of 1 second compared to Euclidean 1.6 seconds and Manhattan 2.4 seconds. Meanwhile, the Euclidean method has the value, Davies Index most optimal which is 0.886 compared to the Manhattan method 0.926 and Chebyshev 0.990.
Genetic Algorithm Optimization on Nave Bayes for Airline Customer Satisfaction Classification Yoga Religia; Donny Maulana
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.925

Abstract

Airline companies need to provide satisfactory service quality so that people do not switch to using other airlines. The way that can be used to determine customer satisfaction is to use data mining techniques. Currently, the website www.kaggle.com has provided Airline Passenger Satisfaction data consisting of 22 attributes, 1 label and 25976 instances which are included in the supervised learning data category. Based on several previous studies, the Naïve Bayes algorithm can provide better classification performance than other classification algorithms. Several studies also state that the use of Naive Bayes can be optimized using Genetic Algorithm (GA) to obtain better performance. The use of Genetic Algorithm for Nave Bayes optimization in classifying Airline Passenger Satisfaction data requires further research to ensure the performance of the given classification. This study aims to compare the use of the Naive Bayes algorithm for the classification of Airline Passenger Satisfaction with and without GA optimization. The data validation process used in this study is to use split validation to divide the dataset into 95% training data and 5% testing data. The test results show that the use of GA on Naive Bayes can improve the classification performance of Airline Passenger Satisfaction data in terms of accuracy and recall with an accuracy value of 85.99% and a recall of 87.91%.
South German Credit Data Classification Using Random Forest Algorithm to Predict Bank Credit Receipts Yoga Religia; Gatot Tri Pranoto; Egar Dika Santosa
JISA(Jurnal Informatika dan Sains) Vol 3, No 2 (2020): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v3i2.837

Abstract

Normally, most of the bank's wealth is obtained from providing credit loans so that a marketing bank must be able to reduce the risk of non-performing credit loans. The risk of providing loans can be minimized by studying patterns from existing lending data. One technique that can be used to solve this problem is to use data mining techniques. Data mining makes it possible to find hidden information from large data sets by way of classification. The Random Forest (RF) algorithm is a classification algorithm that can be used to deal with data imbalancing problems. The purpose of this study is to discuss the use of the RF algorithm for classification of South German Credit data. This research is needed because currently there is no previous research that applies the RF algorithm to classify South German Credit data specifically. Based on the tests that have been done, the optimal performance of the classification algorithm RF on South German Credit data is the comparison of training data of 85% and testing data of 15% with an accuracy of 78.33%.
Analysis of the Use of Particle Swarm Optimization on Naïve Bayes for Classification of Credit Bank Applications Yoga Religia Religia; Gatot Tri Pranoto; I Made Suwancita
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.946

Abstract

The selection of prospective customers who apply for credit in the banking world is a very important thing to be considered by the marketing department in order to avoid non-performing loans. The website www.kaggle.com currently provides South German Credit data in the form of supervised learning data. The use of data mining techniques makes it possible to find hidden patterns contained in large data sets, one of which is using classification modeling. This study aims to compare the classification of South German Credit data using the Naïve Bayes algorithm and compare the classification of South German Credit data using the Naïve Bayes algorithm with particle swarm optimization (PSO). The test was carried out using a confusion matrix to determine the accuracy, precision and recall values of the research model. Based on the test, it is known that PSO is able to increase the accuracy and recall of Nave Bayes, but PSO has not been able to increase the precision value of Nave Bayes. The test results show that PSO optimization gives Naïve Bayes an increase in the value of accuracy by 0.46%, and gives Naïve Bayes an increase in recall value by 3.02%. 
PENGARUH BRAND IMAGE, ELECTRONIC WORD OF MOUTH DAN CELEBRITY ENDORSER TERHADAP KEPUTUSAN PEMBELIAN KONSUMEN PRODUK DAYPACK EIGER DI KOTA BEKASI Yoga Religia; agus sriyanto; Ravindra Safitra Hidayat; Yugi Setyarko
Jurnal Ekonomika dan Manajemen Vol 11, No 1 (2022)
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/jem.v11i1.1745

Abstract

Tujuan penelitian ini adalah untuk menganalisis pengaruh brand image, electronic word of mouth dan celebrity endorser terhadap keputusan pembelian daypack Eiger. Dalam penelitian ini menggunakan metode survey yang terdiri dari 97 responden dengan teknik non probability khususnya purposive sampling. Pengumpulan data menyebarkan kuesioner dan diolah dengan metode deskriptif menggunakan teknik analisis regresi linear berganda. Alat analisis yang digunakan adalah Statistic Product and Service Solution (SPSS) versi 25. Setiap variabel yang di uji telah valid dan reliabel dan telah layak berdasarkan uji asumsi klasik sehingga penelitian dapat dilakukan. Hasil penelitian menunjukan bahwa seluruh variabel bebas (brand image, electronic word of mouth dan celebrity endorser) secara parsial dinyatakan memiliki suatu hubungan yang positif dan terdapat pengaruh signifikan dengan korelasi yang kuat terhadap keputusan pembelian konsumen  Produk daypack Eiger di Kota Bekasi. 
Co-Authors Abdul Mazid Adi Rusdi Agung Nugroho Ahmad Nurhakim Ahmad Nurhakim2 Al Munawir, Al Virizky Arjunitha Alvian Alvin Mubarok Amali Amali Ambarwati, Sri Dwi Ari Anastasya, Ellyn Andi Saputra Anton Dwi Fitriyanto Ari Susanto Arif, Nina Fapari Arvita Emarilis Intani Astuti, Sri Endhri Bella Yoviana Hardini budi purnomo saputro Dani Hamdani Dian Sulistyorini Wulandari Donny Maulana Effendi, Irhas Egar Dika Santosa Erni Ratnasari2 Fathi Habibatur Rahman Firdausi, Asri Sekar Mawar Firdausy, Almer Al Gatot Tri Pranoto Heri Purwanto Heri Susanto Heriyanto2 Heriyanto2 Hidayat, Ravindra Safitra I Made Suwancita Ikhsan Romli Imam Prayogo Pujiono Jayanti, Ansri Jayanto, Nur Dwi Miftakul Huda Muhamad Ekhsan Muhamad Ridwan Muhamad Ridwan Muhammad Asrori Muhammad Irhas Effendi, Muhammad Irhas Nabiela, Ibriza Naingolan, Dedy Sunaryo Nanang Tedi Nunu Nurhasanah Nurrohariah Nurrohariah Pamuji, Windy Anindya Pebrian Pebrian Ramawati, Yussi Said, Jamaliah Siti Khusnul Rifani Siti Suharyani Sriyanto, Agus Subagio, Hani Suganda, Intan Rizkiawati Tasrim, Tasrim Tiara, Luh Putu Tri Mardiana, Tri Triyono Triyono Wahyu Hadikristanto Wisnalmawati Wisnalmawati Yugi Setyarko Zulfikar Muhamad Hasan