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Prediksi Jumlah Hasil Panen Sawit Menggunakan Algoritma Naive Bayes Ananda, Wahyu; Safii, M; Fauzan, M
TIN: Terapan Informatika Nusantara Vol 1 No 10 (2021): Maret 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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

Tujuan dari penelitian adalah untuk memprediksi jumlah hasil panen sawit dan memberikan masukan kepada pihak PTPN IV Dolok Sinumbah untuk lebih memperhatikan upaya dalam menghasilkan jumlah hasil panen sawit yang lebih meningkat setiap tahunnya. Untuk memprediksi jumlah hasil panen sawit metode yang digunakan algoritma Naive Bayes. Sumber data penelitian diperoleh dengan langsung dari instansi terkait. Sehingga diharapkan penelitian ini dapat membantu pihak pimpinan perusahaan dalam memprediksi meningkat atau menurunya produksi hasil sawit. Berdasarkan hasil penelitian yang dilakukan penulis menggunakan metode Naive Bayes pada prediksi meningkat secara manual menghasilkan nilai 7 record. Sedangkan Jumlah prediksi menurun secara manual menghasilkan nilai 5 record. Sehingga total Accuracy yang diperoleh sebesar 100%.

JARINGAN SYARAF TIRUAN DALAM MEMPREDIKSI JUMLAH PRODUKSI DAGING SAPI BERDASARKAN PROVINSI Revi, Ahmad; Solikhun, Solikhun; Safii, M
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 2, No 1 (2018): Peranan Teknologi dan Informasi Terhadap Peningkatan Sumber Daya Manusia di Era
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v2i1.941

Prediction is a process for estimating how many needs will be in the future. This study aims to predict the amount of beef production by province. Beef is one source of protein which is also a high value comodities. Meat production in Indonesia in general tends to increase by around 2.76% per year. But along with the increase in beef production in Indonesia, the level of meat consumption in Indonesia tends to fluctuate in recent years. Imports are the most common step taken by the government to meet domestic beef needs. By using the Artificial Neural Network and backpropagation algorithm, it will be predicted the amount of beef production based on the province in order to determine the steps to meet domestic beef demand based on the amount of beef consumption in the community. This study uses 11 input variables, namely data from 2005 to 2016 with 1 target, data of 2017. Using 5 architectural models to test the data to be used for prediction, the 11-4-1 model, 11-8-1 , 11-18-1, 11-20-1 and 11-28-1. Obtained the results of the best architectural model is the 11-28-1 architectural model with truth accuracy of 100%, the number of epochs 15 and MSE is 0.008623197. This model will be used in predicting the amount of beef production by province.Keywords : Beef production, prediction, backpropagatin, Artificial Neural Network

Sistem Pendukung Keputusan Pemilihan Mekanik Sepeda Motor Yamaha Alfascorfii Dengan Metode Multi Objective Optimization On The Basis Of Ratio Analysis (MOORA) Safii, M; Zulhamsyah, Azlan
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 2, No 2 (2018): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Job performance is one of the needs in a company. The demand for these needs aims to foster consumer confidence in the services provided. In service business activities such as the sale and service of professional motorbikes, mechanical work is needed. In determining the best mechanic, there are many criteria that the mechanic must fulfill. These criteria include efforts to overcome problems, years of service, education and discipline. The selection of the best motorcycle mechanics is done to help improve the mechanical workability to be better than before. To assist with the determination or selection in determining someone who deserves to be the best motorcycle mechanic, a decision support system is needed. In this study a case will be raised which is looking for the best alternative based on the criteria that have been determined by using the Multi Objective Optimization Method On The Base Of Ratio Analysis (MOORA). The research was conducted by looking for weight values for each attribute, then ranking process was carried out which would determine the optimal alternative, namely Yamaha Alfascorfii motorcycle mechanics.

Population Prediction Using Multiple Regression and Geometry Models Based on Demographic Data Safii, M; Setiana, Rika
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 1 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4121

Population growth is an important issue because it significantly impacts a country’s growth and development. Large population growth can impact potential resources that drive the pace of the economy and national development. On the other hand, it can also be a problem of poverty, hunger, unemployment, education, health, and others. The government needs to control population growth to balance it with good population quality. Data sourced from the Population and Civil Registration Office of Simalungun Regency, Tanah Java sub-district has a high population and continues to increase every year. The impact of the population increase is that it affects the population’s welfare, most of whom work as laborers and farmers. To overcome this problem, it is necessary to predict the number of people in the future so that the government can make the right decisions and policies in controlling the population. This study aims to make predictions using two models, namely Multiple Linear Regression, to find linear equations and Geometry Models for population growth projections. This study utilizes multiple regression analysis and geometric models using three independent variables, namely birth rate (X1), migration rate (X2), and death rate (X3), as well as one bound variable, population number (Y). This study’s results show that the Tanah Java sub-district population is expected to increase in the next five years (2024-2028). Predictions show that by 2024, the population is expected to reach 61178 people from 59589 in 2023. Based on the results of the study, the conclusion of this study it can be used as a guide for the authorities in planning strategies and resource allocation and making a significant contribution in estimating population development in the Java region so that there will be no population explosion in the future so that it does not have a negative impact.

Sistem Pendukung Keputusan Pemilihan Mekanik Sepeda Motor Yamaha Alfascorfii Dengan Metode Multi Objective Optimization On The Basis Of Ratio Analysis (MOORA) Safii, M; Zulhamsyah, Azlan
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 2, No 2 (2018): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v2i2.79

Job performance is one of the needs in a company. The demand for these needs aims to foster consumer confidence in the services provided. In service business activities such as the sale and service of professional motorbikes, mechanical work is needed. In determining the best mechanic, there are many criteria that the mechanic must fulfill. These criteria include efforts to overcome problems, years of service, education and discipline. The selection of the best motorcycle mechanics is done to help improve the mechanical workability to be better than before. To assist with the determination or selection in determining someone who deserves to be the best motorcycle mechanic, a decision support system is needed. In this study a case will be raised which is looking for the best alternative based on the criteria that have been determined by using the Multi Objective Optimization Method On The Base Of Ratio Analysis (MOORA). The research was conducted by looking for weight values for each attribute, then ranking process was carried out which would determine the optimal alternative, namely Yamaha Alfascorfii motorcycle mechanics.

Penerapan Algoritma Apriori pada Sistem Informasi Perpustakaan Sekolah SMK Negeri 1 Siantar DwiSyari, Risti; Safii, M; Fauzan, M
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 6, No 1 (2021): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v6i1.287

The SMK Negeri 1 Siantar School Library is one of the special libraries located at the SMK Negeri 1 Siantar School. Libraries provide various kinds of library materials such as books, lessons, lesson questions, and other vocational books. After the researcher made observations, the problem that often occurred was books that were borrowed and returned books that had a non-strategic layout, so that library visitors who did not know the placement found it difficult to find the books they wanted to borrow. This research uses data mining techniques, namely the Apriori Algorithm, the Apriori Method is a method for looking for patterns of relationships between one or more items in a dataset. The Apriori method can be used for data on borrowing books at the Siantar 1 State Vocational School School Library, where the composition of the library books (B1) X_Press UN 2019 B. Indonesia side by side with books (B4) School of Love is a Great Leader and Teacher, if the composition of the book is (B10) Moral Mulia side by side with book (B1) X_Press UN 2019 B. Indonesia, If the book arrangement (B7) X_Press Mathematics is side by side with the book (B5) Relationer, if the book arrangement (B7) X_Press Mathematics is side by side with the book (B9) Indonesian Wisdom Batak Toba, and if the arrangement of the book (B10) Morals Mulia is side by side with the book (B8) Hati Therapy, the data from these items each met the minimum confidance value of 0,5% or the same as the specified 50%. The result of this research is to help library staff arrange the book layout correctly. It is hoped that this research can provide input to the school

Implementasi Metode K-Means pada Hasil Produksi Daging Jenis Ternak Saragih, Siti Nurmila; Safii, M; Suhendro, Dedi
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 6, No 1 (2021): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v6i1.288

Meat production results should have good quantity and quality. To increase meat production, of course it is necessary to look at healthy types of livestock. Meat continues to increase in line with the increase in population, community income, education, standard of living and awareness of the nutritional value of animal production. The need for livestock meat production is one of the driving factors for the economy in Indonesia. This research can provide and input to the local government which is the leading producer of meat for the type of livestock in North Sumatra province and as a basis for making policies to increase meat production for other provinces. The method used in this research is the K-Means Algorithm. Where K-Means is one of the Algorithms in Data Mining that can be used to group data clusters. So that the data from 33 districts / cities will be divided into 2 clusters where cluster 1 is the high group, while cluster 2 is the low group. The results obtained from the study show that the results of manual calculation Algorithms and Microsoft Excel data have the same value, namely high cluster 1 and low cluster 32, and entering Microsoft Excel calculations into rapidminer has the same value as well

Penerapan Algoritma K-Means dalam Proses Clustering Penilaian Kinerja Aparatur Sipil Negera di Sekretariat DPRD Pematangsiantar Aulia, Della; Safii, M; Suhendro, Dedi
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 6, No 1 (2021): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v6i1.270

This study discusses the assessment of the performance of the ASN (State Civil Apparatus) in the Pematangsiantar DPRD Secretariat based on the quality of its work. In carrying out their performance, emoployess are still often truant and have a poor work ethic.In this case the research aims to improve employee welfare. During this time the amount of employee income is only based on the group and position they have. This study uses the K-Means method to classify or classify employee performance quality assessments based on SKP (employee work objectives) with additional income based on work quality assessment and employee behavior.After conducting this research there is a result that in carrying out the performance of employees include Quality, Quantity, Time, Cost of each task activity so as to produce a system capable of assisting the appraisal officer in evaluating the quality of employee performance using the K-Means algorithm as information to find out the employee including very good, enough, and less.

Smart Campus Dropout Prediction: Hybrid Features and Ensemble Approach Safii, M; Nababan, Adli Abdillah; Husain, Husain
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1183

The issue of the high number of students dropping out of college is a major concern in higher education, especially in the smart campus ecosystem. This research aims to design a prediction system for students who are at risk of dropping out by integrating hybrid feature selection methods and ensemble learning that leverage academic data and students' digital footprints. The initial process of model development involves data cleaning and the selection of important features through a combination approach using filter-based methods (mutual information) and recursive feature elimination. A classification model is then designed using the XGBoost and Random Forest algorithms. The testing was conducted using a secondary dataset that included variables such as participation in discussions, attendance rates, interaction with learning materials, and academic achievement. The results of testing with the XGBoost model showed a satisfactory accuracy level, with an F1 score of 0.77 and a ROC AUC of 0.89. The confusion matrix recorded 67 correct predictions for students who graduated and 17 correct predictions for students who dropped out, with a total of 12 misclassifications. These findings suggest that the combination of hybrid feature selection strategies and XGBoost can produce sufficiently accurate predictions of student dropouts and has the potential to be utilized as an early warning system in the governance of a more flexible and responsive smart campus.

Implementasi Data Mining Menggunakan Algoritma Apriori Dalam Menentukan Persediaan Barang : Studi Kasus: Toko Sinar Harahap Tarigan, Putri Mai Sarah; Hardinata, Jaya Tata; Qurniawan, Hendry; Safii, M; Winanjaya, Riki
Jurnal Janitra Informatika dan Sistem Informasi Vol. 2 No. 1 (2022): April - Jurnal Janitra Informatika dan Sistem Informasi
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/janitra.v2i1.142

UMKM ialah kegiatan usaha kecil ekonomi rakyat yang berskala kecil dan dilindungi dari kompetisi usaha yang tak sehat dan tak setara. Wirausaha yang bergerak dibidang pertokoan memiliki prospek yang menjanjikan, karena dapat melayanin masyarakat dengan kategori ekonomi menengah kebawah dan ke atas serta bisa mempermudah masyarakat untuk berbelanja keperluan tiap hari tanpa harus belanja ke supermarket atau swalayan. Namun persediaan barang atau bahan kebutuhan yang tidak dilakukan secara optimal dapat menyebabkan kekosongan pada barang atau bahan kebutuhan tersebut. Hal tersebut juga terjadi pada toko sinar harahap yang sering mengalami kekosongan pada persediaan beberapa barang dan kebutuhan yang di cari oleh pelanggan, ini di akibatkan dari tidak adanya kebiasaan pengontrolan persediaan pada toko. Maka penelitian ini bertujuan untuk melihat barang dan kebutuhan apa saja yang dibutuhkan oleh pelanggan toko. Penelitian ini menggunakan beberapa variabel yaitu tanggal transaksi, nama produk serta jumlah penjualan/pembelian. Maka, dari hasil penelitian menggunakan algoritma apriori tersebut akan di dapat data nama barang yang paling banyak terjual untuk di jadikan sebagai antisipasi persediaan barang agar tidak mengalami kekosongan yang dapat menyebabkan pelanggan kecewa.

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