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All Journal Jurnal Teknologi Informatika dan Komputer Jurnal Indonesia : Manajemen Informatika dan Komunikasi Bulletin of Information Technology (BIT) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Pengabdian Mandiri
Sifa Fauziah
Universitas Pelita Bangsa
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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PREDIKSI PENGANGKATAN KARYAWAN DENGAN METODE ALGORITMA C5.0 (STUDI KASUS PT. MATARAM CAKRA BUANA AGUNG) Ismasari Nawangsih; Sifa Fauziah
Jurnal Pelita Teknologi Vol 16 No 2 (2021): September 2021
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (359.544 KB) | DOI: 10.37366/pelitatekno.v16i2.672

Abstract

In the decision to appoint permanent employees that can be made at the company is determined in terms of attendance and good discipline. Determination of the appointment of permanent employees at PT. Mataram Cakra Buana Agung still looks imprecise and takes a long time. Then a study was conducted that aims to determine the prediction information on permanent employee appointments by looking at the criteria set by the company using the C5.0 classification algorithm with the decision tree method. The data used in this study are employee data owned by PT. Mataram Cakra Buana Agung as many as 403 data, the process of testing the method using Rapid Miner9.5. Based on the results of testing on research in predicting employee appointments at PT. Mataram Cakra Buana Agung, obtained the result from the C5.0 algorithm or decision tree, which is an accuracy of 90.00%. So it can be concluded that the C5.0 algorithm technique with the decision tree method is considered successful in predicting employee appointments at PT. Mataram Cakra Buana Agung. Keywords: Employee recruitment, classification, C5.0 algorithm.
Penerapan Sistem Pakar Berbasis Android Dengan Metode Decision Tree Untuk Memprediksi Postpartum Haemorrhage Pada Wanita Hamil Wiyanto W; Mutiara Ihdina Maulida; Sifa Fauziah
Jurnal Pelita Teknologi Vol 16 No 1 (2021): Maret 2021
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (415.526 KB) | DOI: 10.37366/pelitatekno.v16i1.667

Abstract

Postpartum haemorrhage factor is a contributor to the Maternal Mortality Rate (MMR) 19.7% in the range 12.9 - 28.9 with 480,000 deaths worldwide and 479,000 from developing countries such as Indonesia. In Indonesia the MMR is 305/100,000 Live Births (LB) of the Millennium Development Goals (MDGs) target of only 102/100,000 LB. To achieve the MDGs target, the MMR needs to be lowered, then formulated the problem of how to make an Android-based expert system using the decision tree method so that it can predict Postpartum Haemorrhage from an early age. With the aim of being able to produce an Android-based expert system to predict Postpartum Haemorrhage, so that cases of death caused by Postpartum Haemorrhage receive medical attention from an early age. The expert system makes predictions from logic in an Android-based program using the SDLC structured design system design method and a parallel development model. This logic has gone through the process of classifying a dataset using the Decision Tree method manually and using Rapid Miner. The Decision Tree logic produces three statements of PPH, NO PPH and Potential PPH which are entered using the Java programming language on Android to become an expert system. Pregnant women with predicted PPH and Potential PPH from the expert system can consult a doctor to get the medical personnel they need early to prevent maternal death caused by Postpartum Haemorrhage.
PENGEMBANGAN SISTEM INFORMASI ABSENSI KARYAWAN BERBASIS RFID 125 KHZ MENGGUNAKAN METODE AGILE DEVELOPMENT PADA PT. SANLY INDUSTRIES Ucok Darmanto Soer; Sifa Fauziah; Iim Nursida
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.110

Abstract

The application of absences from work that occurred at PT. Sanly Industries, which has been happening during the pandemic, is still being carried out in each department by a staff. Absenteeism and overtime recording are still manual, and salary summaries are manual. The employee database is not provided with Microsoft Excel, which causes some problems that the system encounters in the company. In addition, handwritten absentee ballots are easily damaged, easily messed up, easily lost, easily damaged, and easily soiled. His RFID-Based Absence with RFID Reader is an alternative for Writers to overcome the problem of absence in the enterprise by using an interface Design using PHP and integrating with a MySQL database. We develop an absence information system using Agile Development methods. The development of this information system supports the processing of employee absences and is integrated with RFID cards that can be used as employee ID cards. The system speeds up the process of reimbursement of absences to help make the payroll process more effective and efficient, making issues related to the payroll process easier and more profitable.
PENERAPAN EDUKASI UNTUK MENINGKATKAN KUALITAS GURU DALAM PROSES PEMBELAJARAN PADA ERA GLOBALISASI Edy Widodo; Sifa Fauziah; Sufajar Butsianto; Agus Suwarno; Andriani Andriani
JURNAL PENGABDIAN MANDIRI Vol. 2 No. 3: Maret 2023
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the world of education there is a lot of knowledge that needs to be learned, especially in the pattern of educating Vocational High School students for all teachers who teach in all learning materials. Teachers are required to be able to provide knowledge and knowledge to all their students, both in behavior, actions, behavior and ethics in learning at school. Especially at this time the rapid development of technology for the world of education, is very influential for all teachers. So teachers must be creative, creative and innovative in their respective fields. In addition, teachers are also required to be able to provide their expertise so that later students will be absorbed in the world of work and work together in DUDI which has been conveyed by the Indonesian Minister of Education.
Analisa Prediksi Hasil Produksi Popok Bayi Metode Naïve Bayes Edy Widodo; Sifa Fauziah; Asep Arwan Sulaeman
Bulletin of Information Technology (BIT) Vol 4 No 1: Maret 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i1.504

Abstract

PT. Elleair Interantional Manufacturing Indonesia is a company engaged in the field of manufacturing baby diapers. With the increasing market demand causing an increase in the production process, what is often experienced is that there is often a lack of finish good product to meet consumer de mand due to delays in the production process. To make it easier for companies to look for factors that can increase production result, the authors coduct research with data mining using the naïve bayes method. In this study the training data and testing data were tested using the RapidMiner application with the naïve bayes algorithm where the tested data were 500 data. Testing is done by calculating the value of precision, recall, AUC dan accuracy using the RapidMiner Application and using Microsoft Excel and calculating the final probability of each class to calculate predictions of product result. With the naïve bayes method we can calculate predictions of production result based on data from the previus year as training data to anticipate shortages in production due to factors that can hider the production process. From the results of the analysis obtained factors that affect production result, namely, the number of materia used for the production of 318 data. The human error factor with the category of “No” as much as 305 data also influences because the less the occurrence of human error the production results are also high. Stop delivery factor with the category “No” as many as 299 data, with fewer cases of stop delivery, the more finish good product that can be sold
Diagnosa Prediksi Penyakit Thypoid Fever Menggunakan Data Mining Dengan Metode Algoritma Naive Bayes Classifier U Darmanto Soer; Sifa Fauziah; Mandasari Aggita
Jurnal Teknologi Informatika dan Komputer Vol 9, No 1 (2023): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v9i1.1611

Abstract

Wabah penyakit Typhoid Fever di Indonesia memang tengah memuncak, penyakit tersebut disebabkan oleh kuman Salmonella Typosa dan menyebar ke manusia melalui makanan dan minuman yang sudah terkotaminasi. Berdasarkan pada data tahun 2018 awal di RS Budi Asih didapatkan bahwa typhoid fever memasuki 3 besar penyakit yang banyak terjadi selama tahun 2018. Seiring dengan banyaknya pasien kasus typhoid fever akan memungkinkan data dengan jumlah skala yang sangat besar dapat terakumulasi, dengan memanfaatkan data tersebut penulis ingin menerapkan salah satu teknik data mining dengan perhitungan statiska dalam melakukan diagnosis penyakit typhoid fever. Metode yang digunakan adalah Naive Bayes dengan menggunakan sebanyak 250 data pasien kasus typhoid fever. Diagnosa Prediksi Penyakit Typhoid Fever menggunakan metode Naive Bayes merupakan aplikasi juga bertujuan membantu masyarakat dalam mendiagnosis penyakit typhoid fever secara dini. Hasil analisis menunjukkan bahwa gejala demam, mual muntah, pusing, batuk, diare, bradikardi bisa menjadi indikator untuk mendiagnosis penyakit typhoid fever. Hasil analisis juga menunjukkan bahwa ketepatan klasifikasi pasien kasus typhoid fever menggunakan metode naive bayes pada penelitian ini adalah sebesar 92%.
Pengelompokan Penerimaan Mahasiswa Baru Dengan Algoritma K-Means Untuk Meningkatkan Potensi Pemasaran Daniel Tambun Daniel; Sifa Fauziah; Muhtadhuddin Danny
Bulletin of Information Technology (BIT) Vol 4 No 3: September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i3.732

Abstract

Utilization of the existing PMB dataset through the clustering method approach can be applied in analyzing the rate of acceptance of new students. The K-Medoid Cluster algorithm model that is applied has results that show a new insight, namely the grouping of new student acceptance rates based on 3 clusters, cluster 1 (C0) is a high level consisting of 49 data from 86 datasets tested and cluster 2 (C1) is a low level consisting of 11 data from 86 datasets tested and cluster 3 (C2) is a medium level consisting of 26 data from 86 datasets tested. The results of the Davies Bouldin Index or DBI value are based on the RapidMiner Studio application obtained from data testing, with a Davies-Bouldin Index evaluation value of 0.769. Keywords: Data Mining, K-Medoid Cluster, Klastrer, PMB
Analisis Sentimen terhadap Pemerintahan Ridwan Kamil sebagai Gubernur Jawa Barat Menggunakan Algoritma Naïve Bayes U. Darmanto Soer; Sifa Fauziah; Sutrisno Sutrisno
Jurnal Teknologi Informatika dan Komputer Vol. 9 No. 2 (2023): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v9i2.1976

Abstract

Penggunaan sosial media di era globalisasi sangat diperlukan bagi sebagian kalangan tidak terkecuali pemimpin daerah. Dalam satu tahun masa jabatanya, Ridwan Kamil mendapatkan berbagai pujian masukan maupun kritikan. Penelitian dilakukan untuk menganalisa sentimen masyarakat terhadap gubernur terpilih Ridwan Kamil. Pengumpulan data dilakukan dengan menggunakan proses crawling data Twitter menggunakan software Orange 3. Tahapan preprocessing terdiri dari proses Remove Duplicates yang bertujuan untuk memfilter data tweet yang sama, dan proses Cleansing yang bertujuan untuk membersihkan data dari noise atau ganguan. Document Processing terdiri dari beberapa proses berikut, Transform Case, Tokenize, Filter Token by Length, Filter Stopwords, Stemming, dan Generate N-Grams. Penelitian ini menggunakan algoritma Naïve Bayes untuk melakukan klasifikasi sentimen dan mencari nilai preference value dikarenakan algoritma tersebut memiliki akurasi yang cukup baik. Dari hasil pengujian yang dilakukan menggunakan teknik cross validation dan pengukuran akurasi menggunakan confusion matrix dengan dilakukan 10 kali pengujian akurasi terbaik yang diperoleh adalah 84,38 %. Respon positif masyarakat terhadap kepemimpinan Ridwan Kamil yang didapatkan dari hasil penghitungan preference value adalah 49%. Sedangkan nilai respon positif tersebut dapat berubah-ubah, dikarenakan respon masyarakat dan data yang diperoleh dapat berubah sewaktu-waktu. Dengan demikian dapat disimpulkan bahwa algoritma Naïve Bayes dapat digunakan untuk melakukan klasifikasi dengan cukup baik dan dapat mengukur respon masyarakat terhadap pemimpin daerah.
Minimalisasi Resiko Kualitas Barang dengan Metode Algoritma Regresi Linier dalam Memprediksi Barang Defect U. Darmanto Soer; Sifa Fauziah; Uswatun Hasanah
Jurnal Teknologi Informatika dan Komputer Vol. 10 No. 1 (2024): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v10i1.2273

Abstract

Barang merupakan sesuatu yang dapat ditawarkan ke pelanggan untuk mendapatkan perhatian untuk dibeli atau digunakan agar dapat memenuhi keinginan dan kebutuhan pelanggan. Pemanfaatan data produk dan defect dapat digunakan dalam melakukan proses tahapan data mining dan pemodelan untuk memprediksi jumlah produk defect di suatu waktu. Penerapan model persamaan algoritma Regresi Linear sederhana dapat diimplementasikan dimana hasilnya juga menunjukkan sebuah wawasan baru bagi kebutuhan prediksi terhadap jumlah produk defect. Model persamaan Regresi Linear sederhana setelah dibandingkan hasil perhitungan secara aktual (observasi) dan juga dengan aplikasi Rapid Miner secara umum menunjukkan kemiripan hasil. Evaluasi dan pengujian performa menggunakan aplikasi Rapid Miner juga dapat menghasilkan gambaran yang relevan dengan skenario yang dimodelkan. Nilai (Root Mean Squared) RMSE juga didapat saat melakukan evaluasi performa model yang diterapkan, dengan nilai RMSE sebesar 0,483 dengan standar deviasi ± 0,0. Rekomendasi yang diberikan adalah: Perbanyak data dalam rentang bulan lebih panjang yang akan digunakan dalam penelitian selanjutnya agar dapat meningkatkan nilai akurasi dari model prediksi terhadap jumlah produk defect dimasa mendatang. Pengembangan model dengan menggabungkan beberapa metode dan algoritma yang dapat digunakan dalam memprediksi sesuatu sehingga diharapkan memberikan hasil yang lebih variatif dan bisa dimanfaatkan dalam pengambilan kebijakan dan strategi pengendalian jumlah produk defect dan meningkatkan nilai RSME dari model prediksi yang diharapkan.
Co-Authors Agus Suwarno Andriani Andriani Asep Arwan Sulaeman Butsianto, Sufajar Daniel Tambun Daniel Edy Widodo Edy Widodo Iim Nursida Mandasari Aggita Muhtadhuddin Danny Mutiara Ihdina Maulida Nawangsih, Ismasari Sutrisno Sutrisno U Darmanto Soer U. Darmanto Soer Ucok Darmanto Soer Uswatun Hasanah Wiyanto Wiyanto