Articles
<![CDATA[Penerapan Algoritma C4.5 Untuk Mengukur Tingkat Kepuasan Mahasiswa ]]>
<![CDATA[Riandari, Fristi]]>;
<![CDATA[Simangunsong, Agustina]]>
<![CDATA[ Jurnal Mantik Penusa Vol 3, No 2,Des (2019): Manajemen Dan Informatika ]]>
Publisher : <![CDATA[Pelita Nusantara Medan ]]>
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<![CDATA[Measuring student satisfaction is very important to note given the high level of competition in the education world as knowledge and technology develops. In measuring student satisfaction it is very important to consider whether the services expected by students are in accordance with what is received. Measuring student satisfaction will greatly help a university in improving the quality of service which will also have an impact on increasing the number of students. The system that is needed is the Application of Data Mining in Measuring Student Satisfaction. Where students will be objects that provide an assessment / opinion on variables that have characteristics, namely Tangiable, Reability, Responsivnes, Assurance, Empathy. This system was built with the C4.5 algorithm and Testing system with RapidMiner 7.5 application rocks]]>
<![CDATA[Diagnosa Penyakit Fibroadenoma Mammae Menggunakan Metode Certainty Factor Dengan Penelusuran Forward Chaining ]]>
<![CDATA[Antonius, Roni]]>;
<![CDATA[Simangunsong, Agustina]]>;
<![CDATA[Sinaga, Anita Sindar RM]]>
<![CDATA[ Jurnal ICT : Information Communication & Technology Vol 18, No 2 (2019): JICT-IKMI, Desember 2019 ]]>
Publisher : <![CDATA[STMIK IKMI Cirebon ]]>
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DOI: 10.36054/jict-ikmi.v18i2.68
<![CDATA[Mammae Fibroadenoma Disease (FAM) is a benign neoplasm that has dense and capsule features. Is the most common type of breast disease. Usually attacks women under 25 years. In this study detection of Fibroadenoma Mammae (FAM) was carried out using the Forward Chaining method. Aims to detect early FAM disease. To prove a certain or uncertain event. The CF hypothesis is influenced by evidence. CF part of the Expert System is suitable for diagnosing something that is uncertain. The CF method can only process 2 weights in one calculation. There are no rules for combining weights, because for any combination the results will remain the same. In the expert system application design method Forward Chaining, CF (H, E) the value of certainty given by experts to a rule, while CF (E, e) is the value of trust given by the user to the symptoms experienced.]]>
<![CDATA[ANALYSIS OF APPLICATION OF TECHNIQUE FOR ORDER PREFERENCE BY SIMILLARITY TO IDEAL SOLUTION IN SELECTING ENERGY SAVE ELECTRICAL LIGHTS BALL FOR HOUSEHOLD: ANALYSIS OF APPLICATION OF TECHNIQUE FOR ORDER PREFERENCE BY SIMILLARITY TO IDEAL SOLUTION IN SELECTING ENERGY SAVE ELECTRICAL LIGHTS BALL FOR HOUSEHOLD ]]>
<![CDATA[Simangunsong, Agustina]]>
<![CDATA[ Jurnal Mantik Vol. 3 No. 4 (2020): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
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<![CDATA[In this bulb selection of users is often confused with the selection of bulbs that are so much circulating in the market, ranging in terms of brand, type, quality, competitive price and with other advantages that in offer often make the users confused to get a light bulb that is energy efficient at an affordable price and with the best quality. Problems not only stop at the time when the bulb placement in each room of the house also often gets a problem. A common problem is inconsistency, which is common when a light bulb has been placed. Watt capacity available in bulb often does not correspond to the size of the room, resulting in less optimal illumination, watt capacity or large power consumption sometimes also does not guarantee to be able to get a good illumination. Therefore it takes a decision support system that can do the calculation of value to be able to help the user determine the bulb that is desired well and precisely according to the needs. This decision support system implements the method of technique for order preference by Simillarity to ideal solution (TOPSIS), which is a method that can provide a weighted and straddles for each of its criteria. With the technique for order preference by Simillarity to ideal solution (TOPSIS) This author creates a system that hopefully can help the decision making in the selection of electric light bulb. Therefore, the authors feel the need for a decision support system to solve the problem of electric bulb selection for the household.]]>
<![CDATA[SISTEM PENDUKUNG KEPUTUSAN PENERIMA BANTUAN PROGRAM KELUARGA HARAPAN (PKH) MENGGUNAKAN METODE TECHNIQUE FOR ORDER OF PREFERENCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS) ]]>
<![CDATA[Raphita Sagala, Jijon]]>;
<![CDATA[Simangunsong, Agustina]]>
<![CDATA[ Riau Journal Of Computer Science Vol. 6 No. 2 (2020): Riau Journal of Computer Science ]]>
Publisher : <![CDATA[Riau Journal Of Computer Science ]]>
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<![CDATA[The poverty that hit Indonesian people became a problem faced by the government, so to overcome poverty since 2007 the Government of Indonesia has implemented the Program Keluarga Harapan (PKH). This Social Protection Program has proven to be quite successful in reducing poverty in Indonesia. In choosing residents in PKH recipients, problems often occur because the data used by PKH from BPS are not up to date, where the data used to determine the PKH recipients in 2019 used data in 2014 which caused new poor families not to receive the Program Keluarga Harapan (PKH). The problem that often occurs is that manual selection requires a lot of time and the selection of participants can be influenced by the objective assessment of PKH companions, the criteria of citizens surveyed are not in accordance with government regulations, and the government determines the number of PKH recipient quotas, so that there are still communities did not accept PKH. To deal with the problem a decision support system is needed to help the Village Government determine PKH recipient residents, one of the decision support system methods is the TOPSIS method. The system is built based on Desktop Applications which can improve data quality and effectiveness of PKH participant selection so that PKH assistance can be received right on target]]>
<![CDATA[PENINGKATAN KEMAMPUAN PENGGUNAAN GOOGLE CLASSROOM SEBAGAI MEDIA PEMBELAJARAN DARING ]]>
<![CDATA[Siahaan, R. Fanry]]>;
<![CDATA[Simanjorang, R. Mahdalena]]>;
<![CDATA[Simangunsong, Agustina]]>;
<![CDATA[Fahmi, Hasanul]]>
<![CDATA[ JMM (Jurnal Masyarakat Mandiri) Vol 5, No 4 (2021): Agustus ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Mataram ]]>
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DOI: 10.31764/jmm.v5i4.5056
<![CDATA[Abstrak: Hingga saat ini virus Corona masih mengkhawatirkan sementara obatnya belum ditemukan. Covid-19 mulai menjadi perhatian masyarakat dunia setelah Januari 2020. Berbagai aspek dan bidang kehidupan manusia mengalami dampak yang sangat besar akibat dari penyebaran dari virus ini, Indonesia salah satu negara yang mengalami dampak tersebut. Salah satu sektor vital yang ditutup karena dianggap rentan untuk penyabaran dari Covid-19 ini adalah sektor pendidikan. Perusahaan raksasa Google memberikan fasiltias pembelajaran bagi siswa yaitu Google classroom. Tujuan pengabdian kepada mayarakat ini adalah untuk meningkatkan kemampuan dan pemahaman siswa SMP KEMALA BHAYANGKARI 1 MEDAN dalam menggunakan Google classroom sebagai media pembelelajaran daring. Metode pelaksanaan dalam kegiatan ini yaitu Students Participatory Appraisal (SPA): 1) Perencanaan 2) Pelaksanaan dan 3) Evaluasi. Dari 73 orang siswa kelas 7 SMP Kemala Bhayankari I Medan yang memahami penggunaan GC sebelum pelatihan sebanyak 6 orang siswa atau setara dengan 8,21%. Setelah dilaksanakan pelatihan jumlah siswa yang memahami penggunaan GC meningkat menjadi 59 orang siswa atau setara dengan 80,82%. Kegiatan ini sebagai perwujudan dari salah satu tridharma perguruan tinggi yaitu pengabdian kepada masyarakat. Abstract: Until now coronavirus is still worrying while the cure has not been found. Covid-19 began to become the attention of the world community after January 2020. Various aspects and areas of human life are experiencing a huge impact as a result of the spread of this virus, Indonesia is one of the countries that experienced the impact. One of the vital sectors that are closed because it is considered vulnerable to the spread of Covid-19 is the education sector. Giant company Google provides students with a learning facility called Google classroom. The purpose of this community service is to improve the ability and understanding of students of SMP Kemala Bhayangkari 1 Medan in using Google classroom as a medium of online learning. The implementation method in this activity is Students Participatory Appraisal (SPA): 1) Planning 2) Implementation and 3) Evaluation. Of the 73 grade 7 students of SMP Kemala Bhayankari I Medan who understood the use of GC before training as many as 6 students or equivalent to 8.21%. After the training, the number of students who understood GC usage increased to 59 students or equivalent to 80.82%. This activity is the embodiment of one of the tridharma of higher education, namely community service.  ]]>
<![CDATA[Penentuan Kepala Program Studi Bisnis Digital Dengan Menerapkan Metode Multi Atribute Utility Theory (MAUT): Penentuan Kepala Program Studi Bisnis Digital Dengan Menerapkan Metode Multi Atribute Utility Theory (MAUT) ]]>
<![CDATA[Simangunsong, Agustina]]>;
<![CDATA[Simanjorang, R. Mahdalena]]>;
<![CDATA[T. Annisa Putri Syahada]]>;
<![CDATA[Laksamana Rinaldi]]>
<![CDATA[ Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Vol 23 No 2 (2024): Agustus 2024 ]]>
Publisher : <![CDATA[PRPM STMIK TRIGUNA DHARMA ]]>
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DOI: 10.53513/jis.v23i2.9978
<![CDATA[Pemilihan ketua program studi di sebuah perguruan tinggi merupakan salah satu elemen penting dalam upaya meningkatkan kualitas pendidikan dan administrasi akademik. Ketua program studi memiliki peran sentral dalam mengkoordinasikan kegiatan akademik, memastikan kurikulum berjalan sesuai dengan standar, dan menjadi penghubung antara mahasiswa dan dosen. Oleh karena itu, proses pemilihan yang transparan dan objektif sangat penting untuk menjamin bahwa yang terpilih adalah individu yang paling kompeten dan sesuai dengan kebutuhan program studi. Untuk mengatasi permasalahan pemilihan Ketua Program Studi dibutuhkan sebuah sistem pendukung keputusan. Salah satu metode sistem pendukung keputusan yang dapat digunakan untuk menyelesaikan masalah tersebut adalah dengan metode MAUT. Penggunaan metode MAUT dapat digunakan untuk memecahkan permasalahan keputusan dengan banyak kriteria. Hasil yang diperoleh dengan menerapkan metode MAUT tersebut adalah dalam pemilihan ketua program studi, maka dapat direkomendasikan alternatif dengan kode A01 dengan nilai preferensi sebesar 0,999 terpilih menjadi ketua program studi bisnis digital.]]>
<![CDATA[The Comparison of the K Mean Algorithm with the C 45 Algorithm in Dataming Applications: Balancing Precision and Speed in Data Mining Solutions ]]>
<![CDATA[Panggabean, Erwin]]>;
<![CDATA[Simangunsong, Agustina]]>;
<![CDATA[Sinaga, Dedi]]>;
<![CDATA[Sihombing, Agus Putra Emas]]>;
<![CDATA[Aritonang, Tri Evalina]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025 ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
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DOI: 10.47709/cnahpc.v7i1.5319
<![CDATA[This research topic discusses the comparison of the K-Means and C4.5 algorithms in the application of data mining to predict aquarium sales in a company. K-Means is a clustering algorithm that functions to group data based on similarity, for example grouping customers based on frequency or type of purchase. This helps companies understand market segments and design marketing strategies accordingly. Meanwhile, C4.5 is a classification algorithm that builds decision trees based on important attributes that influence sales, such as price, season, or promotions. This algorithm is able to predict sales categories, such as increases or decreases, based on historical data. By comparing these two algorithms, the research sought to find out which algorithm is more effective in helping companies predict sales and make strategic decisions. A combination of the two can also be used, with K-Means grouping the data first, then C4.5 classifying each segment formed. These results can provide more accurate sales predictions and more effective marketing strategies. This research is important to understand the effectiveness of algorithms in data mining to improve business decision making.]]>
<![CDATA[Optimizing Network Performance in Cloud Computing Environments Through Dynamic Resource Allocation Strategies ]]>
<![CDATA[Sijabat, Petti]]>;
<![CDATA[Simangunsong, Agustina]]>
<![CDATA[ Dike Vol. 2 No. 2 (2024): Dike Edisi Agustus ]]>
Publisher : <![CDATA[CV. Ro Bema ]]>
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DOI: 10.69688/dike.v2i2.104
<![CDATA[Optimizing Network Performance in Cloud Computing Environments Through Dynamic Resource Allocation Strategies membahas strategi penting untuk meningkatkan kinerja jaringan di lingkungan komputasi awan. Penelitian ini menyoroti pentingnya alokasi dinamis sumber daya jaringan dalam menghadapi permintaan pengguna dan kebutuhan aplikasi yang berfluktuasi. Dengan menerapkan pendekatan ini, penelitian ini bertujuan untuk mengatasi tantangan dalam mengelola sumber daya jaringan secara efisien dan responsif. Melalui analisis data lalu lintas jaringan, pemodelan matematika, dan pengujian di lingkungan simulasi komputer, studi ini mengidentifikasi strategi alokasi sumber daya yang dioptimalkan untuk meningkatkan throughput, mengurangi latensi, dan menghindari kemacetan. Hasilnya menunjukkan bahwa penerapan strategi ini memiliki potensi besar dalam meningkatkan kinerja jaringan secara keseluruhan di lingkungan komputasi awan. Implikasi dari penelitian ini adalah pendekatan dinamis terhadap alokasi sumber daya jaringan dapat memberikan solusi yang efisien dan efektif dalam menghadapi tantangan yang dihadapi oleh infrastruktur komputasi awan. Oleh karena itu, penelitian ini memberikan kontribusi yang signifikan dalam pengembangan teknologi jaringan adaptif dan responsif, yang penting untuk meningkatkan layanan dan aplikasi dalam lingkungan komputasi awan yang berkembang pesat.]]>
<![CDATA[Implementasi Sistem Rekomendasi dengan Collaborative Filtering dalam Pemilihan Produk Skincare ]]>
<![CDATA[Simangunsong, Agustina]]>;
<![CDATA[Simanjorang, R. Mahdalena]]>;
<![CDATA[Fitri Amalia]]>;
<![CDATA[Putri Khairunnisa]]>
<![CDATA[ Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Vol 24 No 1 (2025): Februari 2025 ]]>
Publisher : <![CDATA[PRPM STMIK TRIGUNA DHARMA ]]>
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DOI: 10.53513/jis.v24i1.10706
<![CDATA[Pemilihan produk skincare yang sesuai dengan kebutuhan dan jenis kulit pengguna sering kali menjadi tantangan, terutama dengan banyaknya variasi produk yang tersedia di pasaran. Penelitian ini bertujuan untuk mengembangkan dan mengimplementasikan sistem rekomendasi menggunakan metode Collaborative Filtering dalam membantu pengguna memilih produk skincare yang tepat. Metode Collaborative Filtering dipilih karena kemampuannya dalam menganalisis preferensi dan pola perilaku pengguna berdasarkan data historis dari pengguna lain dengan karakteristik serupa. Sistem ini dirancang dengan memanfaatkan data ulasan, rating, dan profil pengguna untuk menghasilkan rekomendasi yang personal. Hasil pengujian menunjukkan bahwa sistem rekomendasi yang dikembangkan dapat memberikan saran produk yang relevan dengan tingkat akurasi yang memuaskan, berdasarkan evaluasi menggunakan metrik seperti Mean Absolute Error (MAE) dan Root Mean Square Error (RMSE). Dengan implementasi ini, diharapkan pengguna dapat lebih mudah menemukan produk skincare yang sesuai, sehingga meningkatkan kepuasan dan pengalaman berbelanja]]>
<![CDATA[Analisa Dan Implementasi Metode Knowledge Base Recomendation Dalam Penerimaan Karyawan ]]>
<![CDATA[Simangunsong, Agustina]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 1 No. 1 (2019): Computer Networks, Architecture and High Performance Computing ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
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DOI: 10.47709/cnapc.v1i1.48
<![CDATA[Rekomendasi usaha pertama yang dilakukan perusahaan untuk memperoleh karyawan yang qualified dankempeten yang akan ikut sarta mengerjakan semua pekerjaaan pada perusahaan. Untuk mempermudah perusahaan maka perusahaan membuat berbasis web (website) untuk menyeleksi karyawan dimana perusahaan akan mengambil data para pelamar sebagai bahan pertimbangan. Metode Knowledge Based Recommendation sering juga di kenal istilah metode penilaian. konsep dasar metode Knowledge based recommendation adalah mencari penilaian, ketanggapan dan prestasi. Metode ini membutuhkan proses normalisasi keputusan kesuatu skala yang dapat di perbandingkan dengan semua alternative yang ada. sistem rekomendasi ini dirancang untuk memberikan kemudahan pada pelamar dan perusahaan yang akan menggunakan sistem rekomendasi karyawan.]]>
