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Jurusan Sistem Informasi Universitas Islam Negeri Sultan Syarif Kasim Riau
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Prediksi Jumlah Pendaftar Jemaah Umrah Menggunakan Backpropagation dan Regresi Linear pada PT. Hajar Aswad Mubaroq: Prediction of the Registrant Umrah Congregation using Backpropagation and Linear Regression at PT. Hajar Aswad Mubaroq Fikri, M. Hayatul; Permana, Inggih; Mundzir, Mediantiwi Rahmawita; Megawati, Megawati
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1371

Abstract

Umrah adalah perjalanan menuju Baitullah (Ka'bah) di Makkah yang dilakukan untuk melaksanakan serangkaian amal ibadah dengan memenuhi persyaratan-persyaratan khusus. PT Hajar Aswad Mubaroq adalah salah satu agen perjalanan umrah yang secara konsisten siap memberikan layanan kepada calon Jemaah Umrah untuk melakukan ibadah di tanah suci. Pada saat ini PT. Hajar Aswad Mubaroq masih melakukan prediksi manual untuk menghitung prediksi jumlah Jemaah yang akan berangkat umrah. Salah satu akibat dari prediksi manual jumlah pendaftar Jemaah umrah dengan akurat adalah perselisihan jumlah booking pesawat yang terkadang terdapat kekurangan dan kelebihan pemesanan. Sehubungan dengan itu penelitian ini bertujuan memprediksi jumlah Jemaah Umrah PT. Hajar Aswad Mubaroq menggunakan Teknik Machine Learning Agar meminimalkan kesalahan dalam pemesanan penerbangan dan meningkatkan efisiensi analisis serta pengambilan kebijakan terkait data yang ada. Teknik Machine Learning yaitu metode Backpropagation dan Regresi Linear. Hasil penelitian menunjukkan performa terbaik untuk prediksi jumlah pendaftar Jemaah umrah PT. Hajar Aswad Mubaroq yaitu menggunakan algoritma Backpropagation dengan nilai RMSE sebesar 0.101 +/- 0.000, R2 sebesar 0.010 +/- 0.021 dan MAPE 19.74% pada percobaan window size 8.
Perbandingan Performa Algoritma RNN dan LSTM dalam Prediksi Jumlah Jamaah Umrah pada PT. Hajar Aswad: Comparison of RNN and LSTM Algorithm Performance in Predicting the Number of Umrah Pilgrims at PT. Hajar Aswad Al Kiramy, Razanul; Permana, Inggih; Marsal, Arif; Munzir, Medyantiwi Rahmawita; Megawati, Megawati
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1373

Abstract

Secara bahasa umrah bermakna ziarah atau berkunjung, sedangkan secara istilah umrah adalah perjalanan ke Baitullah di luar waktu haji dengan tujuan melaksanakan ibadah tertentu dan memenuhi syarat-syarat khusus. PT Hajar Aswad merupakan sebuah perusahaan travel umrah yang beroperasi di Indonesia. PT Hajar Aswad bertanggung jawab untuk mengatur perjalanan, akomodasi, transportasi, dan berbagai keperluan lainnya bagi para jemaah umrah, untuk itu perlu memiliki pemahaman yang baik mengenai pola dan tren jumlah jemaah umrah agar dapat mengoptimalkan operasional dan memberikan pelayanan yang memuaskan kepada jamaah. Oleh karena itu penelitian ini dilakukan untuk memprediksi jumlah jamaah umrah pada PT Hajar Aswad menggunakan algoritma RNN dan LSTM agar PT Hajar Aswad. . Hasil perbandingan kedua algoritma menunjukkan bahwa LSTM mampu memberikan hasil prediksi yang sedikit lebih baik dibandingkan RNN dengan parameter window size 7, optimizer Adam, batch size 8, dan learning rate 0,01. Model ini memiliki nilai RMSE sebesar 0,1758, MAPE sebesar 0,4846, dan R2 sebesar 0,5198.
Penerapan Algoritma Artificial Neural Network dan Economic Order Quantity dalam Memprediksi Persediaan Pengendalian BBM Ula, Walid Alma; Afdal, M; Zarnelly, Zarnelly; Permana, Inggih
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4916

Abstract

Motor vehicle production in Indonesia increases every year along with increasing demand for fuel as a raw material. Generally, gas stations carry out the process of ordering fuel from Dempo on an irregular basis, the frequency of orders does not have a certain time, orders depend on sales transactions and the amount of fuel inventory available depends on the fuel in storage. Regarding prediction and control of fuel supplies, the risk at gas stations is that the volume of fuel received is different from that ordered. It is suspected that tank trucks carrying fuel during delivery from the depot to gas stations tend to experience evaporation in the tank (loses), so that the fuel quantity decreases. Requests for fuel filling are only based on monitoring without any special calculations resulting in stock being maintained and not covering consumer demand. This research is to analyze the Artificial Neural Network algorithm in predicting fuel, and determine inventory control using Economic Order Quantity. The research was conducted using data from November 2020 - October 2023. The data was processed using the ANN algorithm using Google Colab, and continued with EOQ using Microsoft Excel. The ANN parameters are 1 hidden layer with 100 units, Adam optimizer, learning rate 0.001, batch size 8 and epoch 200. Pertalite ANN test results are MSE 248852593.81 and MAE 12749.45, while Pertamax Turbo MSE 803842.94 and MAE 672, 74 provides predictions for November and December of 11,1436.82 L and 11,1960.83 L and Pertamax Turbo of 3,782.46 L and 3,660.70 L. Furthermore, in 2023 the fuel EOQ of Pertalite and Pertamax Turbo will be 8,445 L and 5,261 L, Safety Stock 3,516 L and 1,064 L, Maximum Inventory 6,042 L and 5,153 L, Re order point 2,403 L and 108 L, Order frequency 149 times and 6 times with Total Inventory Cost Rp. 178,830,302 and Rp. 7,700,459.
Perbandingan Performa Algoritma NBC, C4.5, dan KNN dalam Analisis Sentimen Masyarakat terhadap Krisis Petani Muda pada Media Sosial Facebook Nurkholis, Nurkholis; Permana, Inggih; Salisah, Febi Nur; Mustakim, Mustakim; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6082

Abstract

In Indonesia, young farmers face various challenges and crises that hinder the growth and sustainability of the agricultural sector. They face obstacles such as lack of access to capital, limited technology, climate change, and low selling prices for their crops. In addition, they also often face problems in obtaining accurate and relevant information in an effort to facilitate better decision-making in agricultural businesses, so that the interest of young people today to become farmers is decreasing. The study aims to Compare the Performance of NBC, C4.5, and KNN Algorithms in the Analysis of Public Sentiment towards the Young Farmer Crisis on Facebook Social Media. The application of the K-Fold Cross Validation method is (K = 10). Sentiment analysis is carried out with 3 labels (positive, negative, and neutral). The data used in making the classification model (data from preprocessing the stemming column) using (Google Colab) amounted to 4,878 data with Positive sentiment of 43.13% (2,104), Neutral 39.59% (1,931), Negative 17.28% (843) from the initial data without nested comments, which is 4,981 and the total number of Facebook data is 2,900 likes, 6,700 comments, and 3.3 million viewers. The accuracy of the NBC algorithm is 57.32%, the C4.5 algorithm is 98.42%, and the KNN algorithm (K = 19) is 97.33%. It can be concluded that the results of the comparison of the performance of the three algorithms using (Rapidminer10.3), the C4.5 algorithm gets a higher accuracy of 98.42% and is superior because it produces a decision tree.
Perbandingan Algoritma Support Vector Machine dan Naïve Bayes dalam Menganalisis Sentimen Pinjaman Online di Twitter Ikhsani, Yulia; Permana, Inggih; Salisah, Pebi Nur; Mustakim, Mustakim; Rozanda, Nesdi Evrilyan
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6106

Abstract

Unemployment is one of the poverty factors in society, the large economic needs make it difficult for people to meet their daily needs, thus triggering high demand for loans in society. With the advancement of technology, online loans are now available to help people meet their economic needs. However, over time, many irresponsible parties have taken advantage of this. Marked by the emergence of many illegal online loans, which have triggered negative impacts such as the spread of customer personal data, terror on social media, to debt collection using debt collectors. So that it raises a lot of sentiment in society regarding online loans. For this reason, it is necessary to conduct a sentiment analysis with the aim of public response to online loans, which can be positive, negative or neutral responses. There are two datasets used, namely legal online loans and illegal online loans. This study uses two algorithms, namely SVM and Naive Bayes, the two algorithms will be compared to find out which algorithm is better at analyzing online loan sentiment. In addition, in its use, the two algorithms will also use the SMOTE technique to stabilize the data. The results obtained on legal loan data classification using SVM are quite better than Naive Bayes, with an accuracy rate of 69% with sentiment that often appears is positive sentiment. For illegal loan data, classification using the Naive Bayes algorithm is better than SVM with an accuracy of 75% and sentiment that often appears is neutral sentiment. Based on these results, it can be concluded that in analyzing sentiment using legal loan data, the best algorithm is the SVM algorithm, and for illegal loan data, the best algorithm is the Naive Bayes algorithm.
Analisis Sentimen Tanggapan Publik di Twitter Terkait Program Kerja Makan Siang Gratis Prabowo–Gibran Menggunakan Algoritma Naïve Bayes Classifier dan Support Vector Machine Ramadhani, Annisa; Permana, Inggih; Afdal, M; Fronita, Mona
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6188

Abstract

Indonesia faces a serious challenge related to stunting, with rates reaching 21% in 2024, although this represents a decrease from 24% in 2021. In response, the government has launched various programs to address this issue, including nutrition education, health check-ups for pregnant women, and supplementary food provisions. Amid these efforts, the proposed free lunch program aims to improve nutritional quality for children and pregnant women. However, this program has sparked controversy over the required budget, estimated at IDR 450 trillion, which could impact the national budget balance and lead to inflation.This study analyzes public sentiment toward the free lunch program using the Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms. An analysis of 1,028 tweets revealed that negative sentiment predominates at 44.84%, followed by positive sentiment (32.39%) and neutral sentiment (22.76%). SVM outperformed NBC with an accuracy of 75.39%, compared to NBC's 68.97%. The findings provide important insights into public perceptions of the program and highlight the need for further research to improve sentiment analysis methodologies.
Expert System For Identifying Diseases In Native Chickens Using The Certainty Factor Method Arifin, Abdullah; Novita, Rice; Permana, Inggih; M. Afdal
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.465

Abstract

Farming is the business of breeding and raising animals, divided into two groups: large animals (cows, buffaloes, horses) and small animals (chickens, ducks, birds). The demand for livestock, especially poultry like free-range chickens, is on the rise. However, many small to medium-sized free-range chicken farms still rely on conventional methods for disease treatment, which depend on the experience of the farmers. An expert system is a piece of computer software that mimics the choices and behaviors of a person or group with in-depth knowledge and expertise in a certain field. The objective of this study is to enhance the effectiveness of disease treatment for free-range chickens and streamline the diagnosis procedure. Farmers can determine which diseases are harming their free-range hens by using the Certainty Factor approach. Experts were surveyed to provide the data used in this study. Accurate diagnosis of diseases in free-range chickens and suitable treatment recommendations are provided by the system's diagnostic results.
Analisis Kepuasan Pengguna Aplikasi LinkAja Menggunakan Metode TAM dan EUCS Nisa', Sayyidatun; Megawati; Zarnelly; Permana, Inggih; Marsal, Arif
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4511

Abstract

Aplikasi LinkAja banyak digunakan karena memudahkan bertransaksi. Namun banyak pengguna mengalami kendala seperti, tidak dapat melakukan pembayaran QRIS, transaksi gagal namun saldo sudah terpotong, dan kesulitan mengupgrade ke LinkAja full service. Penelitian ini bertujuan menganalisis tingkat kepuasan pengguna aplikasi LinkAja dengan mengintegrasikan metode Technology Acceptance Model (TAM) dan End User Computing Satisfaction (EUCS). Berdasarkan perhitungan Lemeshow, responden dalam penelitian ini sebanyak 100 orang. Pengumpulan data dilakukan dengan menyebarkan kuesioner kepada pengguna aplikasi LinkAja. Temuan penelitian menunjukkan bahwa 5 hipotesis diterima, yaitu persepsi kemanfaatan, isi, akurasi, bentuk, dan sikap terhadap penggunaan. Sementara 3 hipotesis ditolak, yaitu persepsi kemudahan penggunaan, kemudahan penggunaan, dan ketepatan waktu. Hasil PLS-SEM menunjukkan bahwa kepuasan pengguna memiliki pengaruh positif. Ditunjukkan oleh korelasi kuat antara tiap variabel, dengan nilai R-Square kepuasan pengguna sebesar 84,6%. Ini menunjukkan bahwa aplikasi LinkAja menjalankan fungsinya dengan baik sehingga pengguna merasa puas ketika menggunakannya.
Prediksi Produksi Kelapa Sawit Menggunakan Algoritma Support Vector Regression dan Recurrent Neural Network Alfakhri, Rezky; Permana, Inggih; Novita, Rice; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6441

Abstract

Oil palm is one of the important plantation crops and a leading commodity in Indonesia. PT. XYZ is a company engaged in receiving Fresh Fruit Bunches (FFB) to be processed into Crude Palm Oil (CPO) and Palm Kernel (PK). So far, the company has conducted statistical analysis with a correction value of 5% - 12% on the production results each month in targeting production results. However, this method is still lacking, because it uses manual calculations and considers estimates from personal experience. Therefore, this research proposes a data mining technique with Support Vector Regression (SVR) and Recurrent Neural Network (RNN) algorithms to predict production output precisely. In this study, testing was carried out on SVR hyperparameters, namely Kernel, C, Gamma, and Epsilon. While in RNN, testing is carried out on the optimizer, and the learning rate. In addition, the window size is also determined through a series of experiments, namely 3, 5, and 7. The comparison results show that the RNN model outperforms SVR with an RMSE value of 0.0928, MAPE of 14.32%, and R2 of 0.6164. The RNN model was then implemented to predict the next 3-month period. The prediction results show that there will be a significant increase in production in the first month, then a slight decrease in the second month, and an increase again in the third month.
Prediksi Jumlah Bayi Penerima Imunisasi DPT 1 dan DPT 2 Menggunakan Support Vector Regression Idriani R, Nova; Permana, Inggih; Salisah, Febi Nur; Megawati, Megawati; Rahmawita M, Medyantiwi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7694

Abstract

Vaccination against diphtheria, pertussis (whooping cough), and tetanus is known as DPT immunization, which protects a person from three serious diseases. This vaccine is given in the form of an injection where there are 5 antigens in one injection of the vaccine. DPT immunization is a complete routine immunization that will be continued in grades 1 to 6 elementary school. DPT immunization is feared by mothers because of the side effects that occur in babies after the vaccine injection, namely that the baby will have a fever and be fussy. This has resulted in delays in collecting data on babies who have received this immunization, which has an impact on estimates of babies who will receive DPT immunization in the following month. Of course, this will disrupt the stock of vaccines provided, causing the potential for them to be out of stock. To overcome this problem, it is necessary to collect data on babies who have received DPT in the previous month. This data will be used to predict babies who will receive DPT immunization in the following month using the Support Vector Regression (SVR) method. So that the community health center can provide information regarding the prediction of the number of babies who will receive DPT immunization. This method uses three kernels and a Sliding Window to divide the data into smaller segments, moving alternately across the time series data, making it suitable for predicting babies who will receive DPT immunization in the next time interval. From the three kernels used on the two data that have been separated into DPT 1 and DPT 2, windowing size 3 linear kernels were obtained which were selected as an accurate evaluation of model work on DPT 1 with MAPE values of 3.35, RMSE 0.193, and R2 0.1. And windowing size 3 RBF kernels are more optimal in DPT 2 with MAPE values of 7.86, RMSE 0.163, and R2 0.288.
Co-Authors Ahsyar, Tengku Khairil Al Kiramy, Razanul Alfakhri, Rezky Andaranti, Arifah Fadhila Annisa Ramadhani Arif Marsal Arifin, Abdullah Febi Nur Salisah, Febi Nur Febi Nur Salisah, Febi Nur Salisah Fikri, M. Hayatul Fitriah, Ma’idatul Fitriah, Ma’idatul Idriani R, Nova Ikhsani, Yulia Intan, Sofia Fulvi Jazman, Muhammad Kusuma, Gathot Hanyokro M Afdal M. Afdal M. Afdal Maulana, Rizki Azli Megawati Megawati - Mona Fronita, Mona Muhammad Naufal, Muhammad Mundzir, Mediantiwi Rahmawita Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mutia, Risma Muttakin, Fitriani Nabillah, Putri Nisa', Sayyidatun Nurkholis Nurkholis Priady, Muhamad Ilham Pristiawati, Andani Putri Putra, Moh Azlan Shah Putra, Tandra Adiyatma Rahmawita M, Medyantiwi Rice Novita Rozanda, Nesdi Evrilyan Sabillah, Dian Ayu Salisah, Febi Nur Salisah Salisah, Pebi Nur Susanti, Pingki Muliya Triningsih, Elsa Ula, Walid Alma Windy Amelia Putri Zarnelly Zarnelly Zarnelly