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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Information Systems Engineering and Business Intelligence Tech-E Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Tekno Kompak Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Sisfotek Global Community Development Journal: Jurnal Pengabdian Masyarakat IJPD (International Journal Of Public Devotion) Jurnal Teknologi dan Sistem Tertanam Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Data Mining dan Sistem Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Jurnal Sisfotek Global COMMENT: Journal of Community Empowerment Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Pengabdian kepada Masyarakat (Nadimas) Jurnal Media Celebes Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science
Alita, Debby
Universitas Teknokrat Indonesia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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C45 Algorithm for Motorcycle Sales Prediction On CV Mokas Rawajitu Alita, Debby; Setiawansyah, Setiawansyah; Putra, Ade Dwi
JURNAL SISFOTEK GLOBAL Vol 11, No 2 (2021): JURNAL SISFOTEK GLOBAL
Publisher : STMIK Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v11i2.392

Abstract

CV Mokas Rawajitu is a company that sells various types of used motorbikes both in cash and on credit. In sales, the problem that occurs is the frequent occurrence of ups and downs in motorcycle sales due to the mismatch of the available motorcycle variants with consumer interests so that motorcycle sales often do not reach the target. The role of data mining is needed to analyze consumer purchasing patterns at CV Mokas Rawajitu which can produce information, namely knowing what types of motorbikes most in-demand by consumers are and which are most in-demand in the market by predicting using the C4.5 algorithm based on the sales transaction data they have. from previous periods. The study used a dataset of motorcycle sales at CV Mokas Rawajitu from 2017-2019 with a total data volume of 1,411 data. The attributes used are the motorbike category, the motorbike brand, the motorbike price, and the year of production. The tools used in this research are Rapid Miner. The results of the application of the C4.5 Algorithm can be used as a prediction of sales at CV Mokas Rawajitu because the results of the accuracy of testing data and models using 9-Fold Cross Validation reach a value of 87.95% where the 9th fold reaches the highest accuracy value with a Sensitivity level of 97, 15%, 69.05% Specificity, 86.57% Precision, 12.05% Error (Error Rate) and 30.95% False Positive Rate.
Aplikasi Monitoring dan Penentuan Peringkat Kelas Menggunakan Naive Bayes Classifier Bambang Satrio Gandhi; Dyah Ayu Megawaty; Debby Alita
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 2, No 1 (2021): Volume 2, Nomor 1, 2021
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1184.426 KB) | DOI: 10.33365/jatika.v2i1.722

Abstract

Pendidikan menjadi tolak ukur di sekolah dengan memperlihatkan hasil pencapaian nilai-nilai akademik pada setiap semester. Penilaian akademik juga membutuhkan alat untuk mendukung dalam mengelola data akademik dengan menggunakan teknologi. Dengan pemanfaatan teknologi informasi bisa diterapkan menjadi suatu sistem yang dapat menyimpan dan mengelola dalam waktu lama. Sehingga penulis merancang dan mengembangkan suatu sistem yang pernah dibuat sebelumnya dimana dalam penelitian yang dilakukan adalah aplikasi monitoring dan penentuan peringkat kelas menggunakan Naïve Bayes Classifier. Selain memonitoring nilai-nilai semester siswa, aplikasi ini memberikan informasi akademik, galeri kegiatan kesiswaan, dan menentukan peringkat masing-masing yang dimiliki siswa dikelas. Penelitian menggunakan metode Extreme Programming. Hasil dari perancangan aplikasi monitoring dan penentuan peringkat kelas menggunakan Naïve Bayes Classifier yaitu berupa aplikasi website. Peringkat kelas ditentukan menggunakan Naïve Bayes Classifier dengan beberapa probabilitas dengan akurasi 66.94% menggunakan Rapidminer. Pengujian didapat dengan hasil yang baik dengan Blackbox sebagai fungsional dan ISO 25010 sebagai pengujian usability dan performance. Maka disimpulkan bahwa layak untuk diterapkan dalam memonitoring siswa SMAN 6 Bengkulu Selatan.
SISTEM INFORMASI AKADEMIK DAN ADMINISTRASI DENGAN METODE EXTREME PROGRAMMING PADA LEMBAGA KURSUS DAN PELATIHAN Lisa Ariyanti; Muhammad Najib Dwi Satria; Debby Alita
Jurnal Teknologi dan Sistem Informasi Vol 1, No 1 (2020): Volume 1 No. 1 Juni 2020
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtsi.v1i1.214

Abstract

Kursus dan pelatihan bahasa Korea khusus bagi calon Tenaga Kerja Indonesia yang dilakukan oleh Lembaga Kursus dan Pelatihan Duta Bahasa Korea adalah program untuk pengiriman Tenaga Kerja Indonesia ke Korea Selatan via program G to G (Goverment to Goverment). Pada proses pendaftaran para calon peserta harus datang langsung ke LKP Duta Bahasa Korea untuk mengisi data diri pada form pendaftaran, Selain itu pada proses mendapatkan informasi nilai tryout  peserta harus datang langsung ke LKP Duta Bahasa Korea untuk melihat rekap nilai tryout yang dilaksanakan selama satu bulan. Tujuan dari penelitian ini adalah untuk menghasilkan sistem informasi akademik dan administrasi pada LKP Duta Bahasa Korea dengan metode pengembang sistem Extreme Programming. Sistem yang dihasilkan berbasis website dengan hasil pengujian yang telah dilakukan menggunakan Blackbox testing berdasarkan aspek fungsionality menunjukan bahwa sistem dapat melakukan 94,2% fungsinya dengan benar, yang berarti bahwa sistem layak digunakan. Kata Kunci: Siakad, LKP, Extreme Programming
OPTIMALISASI PEMASARAN UMKM MELALUI E-MARKETING MENGGUNAKAN MODEL AIDA PADA MISS MOJITO LAMPUNG Muhtad Fadly; Suaidah Suaidah; Debby Alita
Kumawula: Jurnal Pengabdian Kepada Masyarakat Vol 4, No 3 (2021): Kumawula: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/kumawula.v4i3.34934

Abstract

Miss Mojito merupakan kelompok usaha UMKM, Miss Mojito mempunyai usaha dalam bidang minuman : soda, kopi, teh, susu dan makanan :kentang goreng,mie, dan ayam geprek.Tujuan pengabdian ini adalah agar Miss Mojito terus berkembang pesat menjadi usahaekonomiproduktif menggunakan aplikasie-marketing. Pembuatan aplikasi menggunakan model AIDA.Attention yaitu memberikan perhatian kepada konsumen dengan cara menampilkan produk dengan kemasan terbaru, menarik perhatian dengan memasarkan produk dengan tampilan yang menarik melalui aplikasi. Interest yaitu dengan aplikasipihak Miss Mojito dapat memberikan infomengenai produknya di aplikasi tersebut.Desireyaitu dengan lebih menyakinkan konsumen untuk membeli produk Miss Mojito, mitra menampilkan promosi di aplikasi dengan cara memberikan diskon dan giveaway. Action yaitu agar konsumen melakukan tindakan pembelian produk Miss Mojito, mitra sudah memberikan pelayanan dalam pemesanan produk melalui jasa GoFood, pembayaran bisa langsung ataupun melalui aplikasi. Aplikasi dapat mereport laporan transaksi penjualan, membuat inovasi baru dengan memberikan kemasan terbaru sehingga konsumen tertarik dan adanya peningkatan penjualan. Aplikasi yang sudah diterapkan pada mitra kami Miss Mojito dan dari segi pemasaran produk sudah cukup banyak dilihat oleh pelanggan dan terjadinya transaksi penjualan.
Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm Auliya Rahman Isnain; Nurman Satya Marga; Debby Alita
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 1 (2021): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.60718

Abstract

 The Indonesian government has enforced the New Normal rule in maintaining economic stabilization and also restraining the spread of the virus during the Covid 19 pandemic. This has become a hot topic of conversation on social media Twitter, many people think positive and negative.The research conducted is a representation of text mining and text processing using machine learning using the Naive Bayes Classifier classification method, the objective of the analysis is to determine whether public sentiment towards the New Normal policy is positive or negative, and also as a basis for measuring the performance of the TF-IDF feature extraction and N-gram in machine learning uses the Naive Bayes method.The results of this study resulted in the accuracy rate of the Naive Bayes method with the TF-IDF feature selection. The total accuracy was 81% with a Precision value of 78%, Recall 91%, and f1-Score 84%. The highest results were obtained from the use of the Naive Bayes and Trigram algorithm parameters, namely 84%, namely 84% Precision, 86% Recall, and 85% f1-Score. The Naive Bayes algorithm with the use of the trigram type N-Gram feature extraction shows a fairly good performance in the process of classifying public tweet data.
Analysis of classic assumption test and multiple linear regression coefficient test for employee structural office recommendation Debby Alita; Ade Dwi Putra; Dedi Darwis
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 3 (2021): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.65586

Abstract

The performance appraisal process in Religious High Court Bandar Lampung has not been carried out objectively, but rather a subjectivity element (relationship closeness). Some employees occupy structural positions but do not fulfil competence and promotion principles, so that it has an impact on providing promotion to a position in the judiciary. Multiple Linear Regression method can provide a predictive model for employee recommendations entitled to occupy positions in the agency. The method implementation using SPSS produces an equation Y = 74.177 + 0.035X1 + 0.020X2 - 0.026X3 + 0.045X4 + 0.001X5. This equation is applied to the employee performance values, and it is obtained from 40 employees 26 employees deserve to be given recommendations promotion. Regression performance testing results using 10-cross validation get the correlation coefficient value is 80.66% with MAE value of 2.24% and RMSE 3.88%, which mean has good performance.
Analysis of Emoticon and Sarcasm Effect on Sentiment Analysis of Indonesian Language on Twitter Debby Alita; Sigit Priyanta; Nur Rokhman
Journal of Information Systems Engineering and Business Intelligence Vol. 5 No. 2 (2019): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2983.476 KB) | DOI: 10.20473/jisebi.5.2.100-109

Abstract

Background: Indonesia is an active Twitter user that is the largest ranked in the world. Tweets written by Twitter users vary, from tweets containing positive to negative responses. This agreement will be utilized by the parties concerned for evaluation.Objective: On public comments there are emoticons and sarcasm which have an influence on the process of sentiment analysis. Emoticons are considered to make it easier for someone to express their feelings but not a few are also other opinion researchers, namely by ignoring emoticons, the reason being that it can interfere with the sentiment analysis process, while sarcasm is considered to be produced from the results of the sarcasm sentiment analysis in it.Methods: The emoticon and no emoticon categories will be tested with the same testing data using classification method are Naïve Bayes Classifier and Support Vector Machine. Sarcasm data will be proposed using the Random Forest Classifier, Naïve Bayes Classifier and Support Vector Machine method.Results: The use of emoticon with sarcasm detection can increase the accuracy value in the sentiment analysis process using Naïve Bayes Classifier method.Conclusion: Based on the results, the amount of data greatly affects the value of accuracy. The use of emoticons is excellent in the sentiment analysis process. The detection of superior sarcasm only by using the Naïve Bayes Classifier method due to differences in the amount of sarcasm data and not sarcasm in the research process.Keywords:  Emoticon, Naïve Bayes Classifier, Random Forest Classifier, Sarcasm, Support Vector Machine
C45 Algorithm for Motorcycle Sales Prediction On CV Mokas Rawajitu Debby Alita; Setiawansyah Setiawansyah; Ade Dwi Putra
JURNAL SISFOTEK GLOBAL Vol 11, No 2 (2021): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (982.039 KB) | DOI: 10.38101/sisfotek.v11i2.392

Abstract

CV Mokas Rawajitu is a company that sells various types of used motorbikes both in cash and on credit. In sales, the problem that occurs is the frequent occurrence of ups and downs in motorcycle sales due to the mismatch of the available motorcycle variants with consumer interests so that motorcycle sales often do not reach the target. The role of data mining is needed to analyze consumer purchasing patterns at CV Mokas Rawajitu which can produce information, namely knowing what types of motorbikes most in-demand by consumers are and which are most in-demand in the market by predicting using the C4.5 algorithm based on the sales transaction data they have. from previous periods. The study used a dataset of motorcycle sales at CV Mokas Rawajitu from 2017-2019 with a total data volume of 1,411 data. The attributes used are the motorbike category, the motorbike brand, the motorbike price, and the year of production. The tools used in this research are Rapid Miner. The results of the application of the C4.5 Algorithm can be used as a prediction of sales at CV Mokas Rawajitu because the results of the accuracy of testing data and models using 9-Fold Cross Validation reach a value of 87.95% where the 9th fold reaches the highest accuracy value with a Sensitivity level of 97, 15%, 69.05% Specificity, 86.57% Precision, 12.05% Error (Error Rate) and 30.95% False Positive Rate.
Multiclass SVM Algorithm for Sarcasm Text in Twitter Debby Alita
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 8 No 1 (2021): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v8i1.646

Abstract

Research in the field of text mining is now increasingly being carried out because of various industries and public figures who want to get information related to public opinion about products or individual assessments obtained from social media, both opinions that are ordinary opinions and sarcasm. In the process of doing text mining, there are many classification methods that can be used, one of which is the Support Vector Machine method which can be optimized so that it can classify data into three classification classes, namely SVM One Against One and One Against Rest. The data used in the study were 2072 data from social media twitter. The results obtained from this study are the accuracy value which has the same value, whether it is done randomly or not randomly, with a value of 60.82% randomized and 60.93% non-random. On other values such as precision, recall and F1 score, the SVM One Against Rest method has a superior value compared to the SVM One Against One value.
Application of Data Mining for Student Department Using Naive Bayes Classifier Algorithm Yohana Tri Utami; Debby Alita; Ade Dwi Putra
Tech-E Vol 5 No 2 (2022): Tech-E
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v5i1.1012

Abstract

SMAN 02 Negeri Agung does not have a system that can assist schools in determining majors. The problem that occurs is that SMAN 02 Negeri Agung, when doing majors, still uses existing data, for example, using a majoring interest questionnaire, there are questions about the interests that students want, and the values of their junior high school report cards, which consist of Indonesian, Mathematics, Science, Social Studies, and English. However, there are still many students who choose majors not based on their interests or historical grades, such as following friends' choices. This can hinder student academic activities in the future, which will affect the value and development of student potential. With this major system, it is hoped that it can help schools and students minimize errors in determining and choosing a major. Based on the problems described above, the authors want to apply the Naïve Bayes method, which will produce a high level of accuracy in determining new student majors more effectively and efficiently.
Co-Authors Adam Indra Sakti Ade Dwi Putra Ade Dwi Putra Ade Setiawan Andi Kurniawan Andi Nurkholis Aris Munandar Asmawati Asmawati Bambang Satrio Gandhi Cholid Fadilah Hasri Dedi Darwis Dinda Titian Lestari Dyah Aminatun Dyah Ayu Megawaty Dyah Ayu Megawaty Emi Suwarni Erliyan Redy Susanto Fadila Shely Amalia Feby Amanda Fenty ariany Fikih Yuhada Sena Hayatunnufus Hayatunnufus Heni Sulistiani Imam Ahmad Indah Sari Irwan Tubagus Isnain, Auliya Rahman Jepi Supriyanto Lathifah L Lathifah Lathifah Lisa Ariyanti Mandasari, Berlinda Miswanto Miswanto Muhammad Najib Dwi Satria Muhammad Pajar Kharisma Putra Muhammad Saefudin Muhtad Fadly Ni Made Suka Rani Nur Rokhman Nurman Satya Marga Nurman Satya Marga Nuroji Nuroji Permata Permata Prita Dellia Puspaningtyas, Nicky Dwi Putra, Ade Dwi Putri Sukma Dewi Rahmanto, Yuri Raras Kartika Sari RB Ali Shodiqin Reza Pustika Rillya Arundaa Riska Amalia Praptiwi S. Samsugi Setiawansyah Setiawansyah Sigit Priyanta Styawati Styawati suaidah suaidah Taufiq Martiwansyah Tenda, Edwin YOHANA TRI UTAMI, YOHANA TRI Yuli Santika Yusra Fernando