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Improved Naive Bayes Algorithm with Particle Swarm Optimization to Predict Student Graduation Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Sari, Anggi Puspita
IJISTECH (International Journal of Information System and Technology) Vol 7, No 6 (2024): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i6.338

Timely graduation is very important for educational institutions such as universities, especially for students. Because it can prove that the University and students are able to undergo the learning process theoretically and practically. But many students do not pay attention to graduation, especially those who are already working or married. Therefore, analysis is needed to predict student graduation so that solutions can be found by the University. Data mining was chosen as a method to process data to get new information. The algorithm used in data mining is Naïve Bayes. The research stages include loading data into excel, cleaning empty data, selecting databases related to graduation and taking data from 300 students majoring in Informatics Engineering. The next stage is data transformation by categorizing student data, namely personal data attributes (gender, age, marital status, job status) and academic data (grade). Data testing, application of Naïve Bayes algorithm and accuracy testing were carried out with Rapis Miner software version 10.3.001. The results of data processing with Rapid Miner using the Naïve Bayes algorithm are shown with the Confusion Matrix and ROC Curve. The results of confusion matrix from data processing with Naïve Bayes in the form of accuracy, precision, and recall have the same result of 100%. The percentage of the Confusion Matrix indicates that the model created can classify correctly and accurately. The ROC curve depicted with AUC yields a value of 1, which means that the test showed excellent results

Penerapan Metode Simple Additive Weighting (SAW) Dalam Menentukan Lokasi Usaha Wati, Embun Fajar
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.316

For an entrepreneur, choosing a strategic business location is one of the main keys in establishing a business. In determining the location to choose, there are several considerations or criteria. This criterion will be the basis for choosing a location, and of course in choosing a location takes a long time because the criteria given can be very diverse. To be more effective and efficient, a Decision Support System (DSS) is needed that serves to help entrepreneurs in determining which location is most suitable. In this research the method in spk used is Simple Addidtive Weighting (SAW). SAW processes data by giving weight to the criteria used as a reference in decision making. After that, a matrix of decisions is made based on criteria. The results of the research can be the best recommendation for entrepreneurs to establish a strategic place of business in accordance with their wishes. The sequence of location selection resulting from the application of this SAW method is Teluk Naga with a final result of 6, followed by Poris with a final value of 5.66667, and lastly Dadap with a final value of 5.

Customer Loyalty Classification with Comparison of Naive Bayes, C4.5, and KNN Methods Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Indriyani, Luthfi
IJISTECH (International Journal of Information System and Technology) Vol 8, No 3 (2024): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i3.361

Customer loyalty is indispensable for the survival of a company. Customer loyalty needs to be maintained in order to return to visit and transact with the Company. Customer data consisting of age, annual income, purchase amount, region, purchase frequency, and loyalty score features can produce new information, namely analyzing customers who have high loyalty. Data processing is carried out using three data mining algorithms, namely Naïve Bayes, C4.5 or Decision Tree, and KNN. The stages carried out in data processing consist of data selection, preprocessing, transformation, and modelling. The customer data used amounted to 238. Modelling is carried out using Rapid Miner Software. Customer loyalty classification can be easily done with the three algorithms, namely Naive Bayes, and C4.5 or Decision Tree, and KNN which is validated by the 10-fold cross-validation method so as to produce the highest percentage of accuracy and the similarity of the accuracy value of the Naive Bayes and C4.5 algorithms, which is 96.67%. In the AUC value, it can be seen that the Naive Bayes algorithm is superior to the C4.5 algorithm or Decision Tree and KNN. The result of the highest AUC value is 0.997, the highest precision percentage is 98.92% achieved by the Naive Bayes algorithm. The result of the highest recall percentage is C4.5 of 100%. The results of the AUC value and accuracy percentage on the three algorithms prove that the performance of the three algorithms is very good.

Penerapan Metode Simple Additive Weighting (SAW) Dalam Menentukan Lokasi Usaha Wati, Embun Fajar
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.316

For an entrepreneur, choosing a strategic business location is one of the main keys in establishing a business. In determining the location to choose, there are several considerations or criteria. This criterion will be the basis for choosing a location, and of course in choosing a location takes a long time because the criteria given can be very diverse. To be more effective and efficient, a Decision Support System (DSS) is needed that serves to help entrepreneurs in determining which location is most suitable. In this research the method in spk used is Simple Addidtive Weighting (SAW). SAW processes data by giving weight to the criteria used as a reference in decision making. After that, a matrix of decisions is made based on criteria. The results of the research can be the best recommendation for entrepreneurs to establish a strategic place of business in accordance with their wishes. The sequence of location selection resulting from the application of this SAW method is Teluk Naga with a final result of 6, followed by Poris with a final value of 5.66667, and lastly Dadap with a final value of 5.

Customer Loyalty Classification with Comparison of Naive Bayes, C4.5, and KNN Methods Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Indriyani, Luthfi
IJISTECH (International Journal of Information System and Technology) Vol 8, No 3 (2024): The October edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i3.361

Customer loyalty is indispensable for the survival of a company. Customer loyalty needs to be maintained in order to return to visit and transact with the Company. Customer data consisting of age, annual income, purchase amount, region, purchase frequency, and loyalty score features can produce new information, namely analyzing customers who have high loyalty. Data processing is carried out using three data mining algorithms, namely Naïve Bayes, C4.5 or Decision Tree, and KNN. The stages carried out in data processing consist of data selection, preprocessing, transformation, and modelling. The customer data used amounted to 238. Modelling is carried out using Rapid Miner Software. Customer loyalty classification can be easily done with the three algorithms, namely Naive Bayes, and C4.5 or Decision Tree, and KNN which is validated by the 10-fold cross-validation method so as to produce the highest percentage of accuracy and the similarity of the accuracy value of the Naive Bayes and C4.5 algorithms, which is 96.67%. In the AUC value, it can be seen that the Naive Bayes algorithm is superior to the C4.5 algorithm or Decision Tree and KNN. The result of the highest AUC value is 0.997, the highest precision percentage is 98.92% achieved by the Naive Bayes algorithm. The result of the highest recall percentage is C4.5 of 100%. The results of the AUC value and accuracy percentage on the three algorithms prove that the performance of the three algorithms is very good.

Comparison of Naive Bayes and C4.5 Methods with Particle Swarm Optimization on Customer Loyalty Classification Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Indriyani, Luthfi
IJISTECH (International Journal of Information System and Technology) Vol 8, No 6 (2025): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i6.382

The Company attaches great importance to customer loyalty for the sustainability of the Company. Loyal customers will buy many times and provide great profits. In this study, the decision tree method or C4.5 and naïve bayes were used with PSO optimization for customer classification which aims to design a strategy in decision-making towards disloyal customers. Some of the stages carried out are data load into MS. Excel, data cleaning from noise, data selection as many as 238 obtained from previous research with several attributes, including, namely age, annual income, purchase amount, region, purchase frequency, and loyalty score, as well as data transformation, namely each attribute is grouped into 2 with their own criteria, data testing by modeling data through Rapidminer, Data evaluation by examining the values of accuracy, precision, recall, and AUC. Both methods have the same accuracy value of 96.67% and the same recall value of 100%. For the precision value, there is a difference of 0.6% and the precision decision tree value is higher than the naïve Bayes which is 96.16%. As for the AUC value, it is higher naïve bayes, which is 0.922 with the difference from the decision tree of 0.059. It can be concluded that the two methods in processing customer loyalty data in this study have the same accuracy, so both methods are equally good.

Comparison of Naive Bayes and C4.5 Methods with Particle Swarm Optimization on Customer Loyalty Classification Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Indriyani, Luthfi
IJISTECH (International Journal of Information System and Technology) Vol 8, No 6 (2025): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i6.382

The Company attaches great importance to customer loyalty for the sustainability of the Company. Loyal customers will buy many times and provide great profits. In this study, the decision tree method or C4.5 and naïve bayes were used with PSO optimization for customer classification which aims to design a strategy in decision-making towards disloyal customers. Some of the stages carried out are data load into MS. Excel, data cleaning from noise, data selection as many as 238 obtained from previous research with several attributes, including, namely age, annual income, purchase amount, region, purchase frequency, and loyalty score, as well as data transformation, namely each attribute is grouped into 2 with their own criteria, data testing by modeling data through Rapidminer, Data evaluation by examining the values of accuracy, precision, recall, and AUC. Both methods have the same accuracy value of 96.67% and the same recall value of 100%. For the precision value, there is a difference of 0.6% and the precision decision tree value is higher than the naïve Bayes which is 96.16%. As for the AUC value, it is higher naïve bayes, which is 0.922 with the difference from the decision tree of 0.059. It can be concluded that the two methods in processing customer loyalty data in this study have the same accuracy, so both methods are equally good.

Pemanfaatan Teknologi Digital untuk Meningkatkan Pengetahuan dan Keterampilan Kader Posyandu: Pengabdian Mike Indarsih; Embun Fajar Wati; Budi Sudrajat; Susanti
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 1 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 1 (Juli 2025 -
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i1.2599

Mawar A Posyandu Partner is located in Pengayoman Complex, Jl. RW 13A, Sukasari Subdistrict, Tangerang City, and has various service and outreach activities. The Posyandu partner has several problems, namely 1) the absence of social media as a means of disseminating information to educate mothers, 2) the absence of interesting content to be disseminated online to educate mothers, especially for mothers who cannot come or are busy working. The solutions needed to overcome all these problems are, 1) the creation and use of Instagram social media as a means of socializing posyandu activities and educating mothers, 2) creating video content with the capcut application and creating image and written content with the canva application (video, image, and written content will be uploaded to partner Instagram. The PM implementation with 4 lecturers and 2 students, with the following workflow, 1) Situation analysis with observation and interviews, 2) Designing solutions with information technology, 3) Training, mentoring, and socialization, 4) Evaluation by monitoring activities. This activity resulted in increased knowledge and skills that produced video content of posyandu activities and flyers about the posyandu activity schedule.

PENGEMBANGAN PROMOSI DIGITAL PONPES MENGGUNAKAN DESAIN FLYER YANG MENARIK DENGAN CANVA Wati, Embun Fajar; Sudrajat, Budi; Indarsih, Mike; Fadholi, Arief
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 5 (2024): Vol. 5 No. 5 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i5.35150

Ponpes yang didirikan oleh Yayasan Bani Salam Indonesia pimpinan Ust. Hasanuddin berada di Cipondoh, Tangerang dan memiliki jumlah santri sebanyak 31 dan guru berjumlah 6 orang. Ponpes mempunyai beberapa permasalahan ponpes salah satunya adalah promosi digital yang belum optimal untuk penerimaan santri/wati baru. Beberapa masalah lainnya yaitu, pencatatan laporan keuangan yang masih dengan buku, pencatatan penilaian santri/wati yang masih dengan buku, dan sharing pengumuman, foto, dan video kegiatan santri/wati yang tertumpuk chat grup Whatsapp. Tetapi yang akan dibahas disini adalah masalah promosi digital. Solusi yang dibutuhkan untuk mengatasi permasalahan tersebut adalah pembuatan desain flyer penerimaan santri/wati untuk promosi digital di medsos dengan canva dan penyebarannya melalui Instagram. Metode yang digunakan adalah analisa situasi dengan mengajukan beberapa pertanyaan, perancangan solusi yaitu membuat desain flyer dengan canva dan menyebarkan ke Instagram, pelatihan dan pendampingan kepada pengurus ponpes yang bertanggung jawab dengan promosi, dan evaluasi kegiatan dengan memberikan kuesioner kepada mitra juga memonitor perkembangan kegiatan setelah pelatihan dan pendampingan. Program Kemitraan Masyarakat dilakukan oleh 4 orang dosen dan 2 orang mahasiswa. Salah satu luaran yang dihasilkan dari PKM yang dibiayai dari hibah internal Universitas Bina Sarana Informatika ini adalah desain flyer. Hasil dari penyebaran kuesioner yaitu peningkatan pengetahuan 87%, keterampilan 93%, dan pelayanan 87%.

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