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All Journal Jurnal Sosial Ekonomi Pertanian JUSIFO : Jurnal Sistem Informasi Annual Research Seminar CCIT (Creative Communication and Innovative Technology) Journal Sriwijaya Journal of Informatics and Applications Jurnal Generic
Yunita Yunita
Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Kombinasi Analytical Network Process (ANP) dan Technique For Others Reference by Similarity to Ideal Solution (TOPSIS) untuk Menentukan Penerima Bantuan Rastra Yunita Yunita; Rusdi Efendi; Evita Hardanita
JUSIFO : Jurnal Sistem Informasi Vol 7 No 1 (2021): JUSIFO (Jurnal Sistem Informasi) | June 2021
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v7i1.8440

Abstract

The problem of poverty is still a priority for the Indonesian government. The Indonesian government has launched several programs to assist in alleviating poverty, one of which is the rice program for prosperous families, called Rastra. In the implementation of the Rastra program, it should be carried out objectively so that the distribution of the program can be right on target, but there are still problems in determining the recipients and the accuracy of the distribution of Rastra program. This study aims to combine the ANP and TOPSIS methods in helping to provide recommendations for recipients of Rastra program, besides that in this study also measuring the level of accuracy between the implementation carried out with the real data. In this study, a combination of ANP and TOPSIS methods was used in decision making. The ANP method is used to determine the weight of the criteria used, while the TOPSIS method is used to determine the recommendations for recipients of the Rastra program. The accuracy test was carried out in 13 villages in the Gunung Megang, Muara Enim. The results of the tests carried out obtained an average of 84.10%. This shows that the combination of these two methods can be applied as a solution in providing recommendations for recipients of the Rastra program.
KARAKTERISTIK KONSUMEN DAN PREFERENSINYA TERHADAP ATRIBUT BERAS BERDASARKAN GOLONGAN TINGKAT PENDAPATAN DI KOTA PALEMBANG Yunita Yunita; Muhammad Arbi
Jurnal Sosial Ekonomi Pertanian (J-SEP) Vol 12 No 3 (2019)
Publisher : University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/jsep.v12i03.14500

Abstract

Ringkasan Kajian dilakukan terhadap 150 responden yang bermukim di wilayah Kota Palembang yang dipilih secara acak berdasarkan asumsi kelompok masyarakat yang memiliki golongan pendapatan tinggi (50 responden), golongan pendapatan sedang (50 responden), dan golongan pendapatan rendah (50 responden). Tujuan kajian adalah untuk mendeskripsikan karakteristik konsumen dan menganalisis preferensi konsumen terhadap atribut beras berdasarkan golongan tingkat pendapatan rumah tangga di Kota Palembang. Kajian dirancang dengan metode survey, menggunakan kuisioner sebagai pedoman wawancara terhadap responden. Hasil penelitian diketahui bahwa Karakteristik konsumen beras berdasarkan golongan tingkat pendapatan rumah tangga di Kota Palembang pada umumnya memiliki usia antara 15-56 tahun, berjenis kelamin perempuan, berpendidikan dari yang Tidak Bersekolah sampai ada yang Lulus Perguruan Tinggi, mayoritas berprofesi sebagai ibu rumah tangga dan BUMN, berpendapatan antara <Rp1.000.000,- sampai dengan >Rp10.000.000,- per bulan, dengan rata-rata konsumsi beras antara 1-10 Kg/bulan sampai dengan 51-60 Kg/bulan. Hal ini menunjukkan bahwa karakteristik rumah tangga dari ketiga tingkat golongan, baik dari golongan pendapatan tinggi, sedang maupun rendah sangat beragam yang mana karakteristik ini dapat mempengaruhi keputusan memilih dan membeli beras yang akan dikonsumsi. Atribut beras mencakup tingkat kepulenan nasi, daya tahan simpan nasi, rasa nasi, aromatic, jenis beras, volume keterkembangan, beras kepala, butir patah, butir menir, butir kapur, dan warna. Preferensi konsumen rumah tangga berdasarkan tingkat kepentingan atribut beras untuk kategori sangat penting yang paling banyak dipilih pada golongan pendapatan tinggi dan sedang adalah kualitas sebelum beras tersebut menjadi nasi, sedangkan golongan pendapatan rendah adalah factor ketahanan pada nasi. Preferensi konsumen rumah tangga berdasarkan tingkat kesukaan atribut beras untuk kategori sangat suka yang paling banyak dipilih pada golongan pendapatan tinggi adalah rasa nasi, untuk golongan pendapatan menengah adalah kepulenan nasi, dan golongan pendapatan rendah adalah kepulenan nasi dan beras kepala
Peringkasan Teks Berita Berbahasa Indonesia Menggunakan Metode Latent Semantic Analysis (LSA) dan Teknik Steinberger&Jezek Jerry Satiamy Saputra; Muhammad Fachrurrozi; Yunita Yunita
Annual Research Seminar (ARS) Vol 3, No 1 (2017): ARS 2017
Publisher : Annual Research Seminar (ARS)

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Abstract

Dokumen berita merupakan dokumen yang memuat berbagai macam informasi. Semakin banyak informasi yang terdapat pada suatu dokumen membuat dokumen tersebut semakin panjang. Membaca keseluruhan dokumen tersebut memakan banyak waktu. Ringkasan dokumen diperlukan untuk memudahkan memahami informasi yang berukuran besar dengan cepat. Peringkasan dokumen secara otomatis merupakan solusi untuk membantu mendapatkan intisari dari dokumen. Pada penelitian ini dilakukan penerapan metode Latent Semantic Analysis dan teknik Steinberger&Jezek yang digunakan untuk peringkasan teks otomatis. Jumlah data uji yang digunakan sebanyak 10 teks berita yang diambil dari data uji penelitian sebelumnya. Hasil penelitian yang telah dilakukan menghasilkan rata-rata recall 0.7027, precision 0.6973, dan f-measure 0.6974.
Indonesian-English Machine Translation Using Rule-Based Method Novi Yusliani; Yunita Yunita; Wenty Octaviani
Annual Research Seminar (ARS) Vol 1, No 1 (2015)
Publisher : Annual Research Seminar (ARS)

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Abstract

Rule-Based Machine Translation (RBMT) used a set of linguistic information to translate source language to target language. POS tagger and Shift-Reduce-Parsing (SRP) could be used to get the linguistic information. POS tagger was used to get word class of each word in sentence and SRP was used to get the function of each word in sentence. SRP was also used to get the structure of sentence. In this research, POS tagger and SRP were used to get the linguistic information of source sentence. Translation process was done by using billingual dictionary. Last, a set of rules was used to generate the target sentence. The accuracy of Indonesian-English machine translation was 100% for the S-P-Adv pattern, but for the S-P pattern and S-P-O pattern is 93,33%.
Implementasi Metode Analytical Hierarchy Process Dan TOPSIS Dalam Sistem Pendukung Keputusan untuk Pembelian Mobil pada Rental Mobil Dicky Ahmad Rizaldi; Yunita Yunita; Desty Rodiah
Generic Vol 12 No 1 (2020): Vol 12, No 1 (2020)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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Abstract

Car rental is a business engaged in services that provide car rental services. Car rental owners must be selective in choosing a car to be used as a fleet, because if the car chosen is not right, the rental owner will experience a loss. To solve this kind of problem, a system to support a decision is a solution that can help rental owners according to their wants or needs. One approach that can be used in this case is the Decision Making System using the Analytical Hierarchy Process (AHP) method and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The Analytical Hierarchy Process (AHP) method was used as weighting and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as a ranking. Testing to get a fairly good accuracy using the AHP and TOPSIS methods.
Implementation of K-Nearest Neighbor Method and Weighted Product Method in Determining High School Majors Kartika Rahmayani; Yunita Yunita; Kanda Januar
CCIT Journal Vol 15 No 2 (2022): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (612.893 KB) | DOI: 10.33050/ccit.v15i2.2116

Abstract

High school education in Indonesia is divided into several majors that have been determined by the ministry of education. The Major will also have an influence on students when they will continue their education to the university level. Therefore, students must be placed in majors that are in accordance with their abilities and desires so that they can complete their education well. To assist the school in providing advice on the division of student majors and provide more accurate results, the authors conducted research using the K-Nearest Neighbor method which will classify students so that they are classified into students majoring in science and social studies. K-Nearest Neighbor is used because it can classify student testing data in the case of class 2020 by adapting solutions from student training data in cases of class 2019 based on the data they have. Furthermore, so that student data that has been classified can be sorted based on the best value so that class division can be carried out according to the results of the sequence of students in each majors, the Weighted Product method is used. The Weighted Product method sorts student data based on criteria values that have different weight values. The results in this study provide the highest accuracy value for the K-Nearest Neighbor method using the k value configuration of 88% and the accuracy value 84% for using the Weighted Product method.
Comparison of Certainty Factor (CF) and Case Based Reasoning (CBR) to Diagnose Infertility in Women Risky Tama Putri; Yunita Yunita; Osvari Arsalan; Rizki Kurniati
Sriwijaya Journal of Informatics and Applications Vol 3, No 1 (2022)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v3i1.28

Abstract

Infertility has now become a terrible and serious problem for women. Limited information about infertility suffered by women makes it difficult for them to predict the disease they are suffering from. Therefore we need an expert system that can predict infertility in women. The methods used in this research are Certainty Factor (CF) and Case Based Reasoning (CBR) methods. Certainty Factor (CF) is one of the techniques used to overcome uncertainty in decision making. Case Based Reasoning (CBR) is a problem solving method by remembering similar events that happened in the past and then using that knowledge or information to solve new problems. Based on the test results using 25 test data, the accuracy of the expert system for diagnosing infertility in women using the Certainty Factor (CF) method is 92%, while the curation of the expert system for diagnosing infertility in women using the Case Based Reasoning (CBR) method is 76%. 
BEST EMPLOYEE ASSESSMENT DECISION SUPPORT SYSTEM USING ANALYTICAL HIERARCHY PROCESS (AHP) AND ADDITIVE RATIO ASSESSMENT (ARAS) METHODS Muhammad Rizkiansyah; Yunita Yunita; Nabila Rizky Oktadini
Sriwijaya Journal of Informatics and Applications Vol 3, No 1 (2022)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v3i1.33

Abstract

The purpose of this research is to make it easier to solve the problem of evaluating the best employees in the company PT. ASA KARYA MULTIGUNA, therefore a decision support system is needed. The Analytical Hierarchy Process (AHP) method is used for weighting criteria and the Additive Ratio Assessment (ARAS) method is used for ranking alternatives. From the results of the weighting of the criteria obtained weights for ability (0.31), initiative (0.04), discipline (0.08), performance (0.21), responsibility (0.13), attendance (0.08), communication (0.04), attitude (0.08). From the results of the alternative rankings, for the November 2020 period, the first place was Hendri Gustian, the second was Eka Wingsati Sartono, and the third was Eva Maya Fadila. In the December 2020 period, the first place was Hariyadi, the second was Hendri Gustian, and the third was Deden Kurniawan. In the January 2021 period, the first rank was Deden Kurniawan, the second rank was Hilman Djuniarto, and the third rank was Nurhayati Natalia. From the data for 3 periods from November 2020 to January 2021, which were tested managed to the average confidence level is 84.1%.
MEMBER ELECTION DECISION SUPPORT SYSTEM SOUTH SUMATERA PASKIBRAKA USING TOPSIS-PROMETHEE METHOD Angga Adiningrat Mulyanata; Yunita Yunita; Desty Rodiah
Sriwijaya Journal of Informatics and Applications Vol 3, No 2 (2022)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v3i2.41

Abstract

Paskibraka is the best young generation selected through various selections to raise and lower the Heritage Flag on Indonesian Independence Day. However, in the enthusiasm of the students to take part, the Dispora of South Sumatra Province still uses a manual assessment system so that several obstacles were found in its implementation. done with Microsoft Excel, as well as a calculation system that can only be used for one period, while this selection is an annual event that is held every time to celebrate Indonesian Independence Day. Therefore we need a way that can help the Dispora of South Sumatra Province in determining the best alternative for paskibraka members. One algorithm that is useful in decision support is Topsis. Topsis is used in the application of values for each criterion and a different range of values. Then using the Promethee method can improve the Topsis method because the Promethee method is used to determine the order of priority in multi-criteria analysis. The data taken by 60 participants were then researched according to predetermined criteria including written test scores, interview tests, health tests, physical fitness, and posture. Produced the best participants according to the system as many as 15 data. The results of the research test have an accuracy of 80%.
Implementation of K-Means and SAW Methods in Determining Non-Cash Food Aid Recipients Yunita Yunita; Rizki Kurniati; Desty Rodiah; Allsela Meiriza; Luh Sri Mulia Eni
CCIT Journal Vol 16 No 2 (2023): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v16i2.2525

Abstract

Determination of prospective non cash food assistance recipients, especially in Air Talas village, still uses a manual system so that in the process of determining the recipient there is a risk that the recipient will be inaccurate, so that the village government needs a system that can assist the process of determining prospective non cash food assistance recipients. This study aims to implement the K-Means and SAW methods in determining recipients of non cash food assistance in Air Talas village. The benefits of this research can help the Air Talas village government in determining and recommending prospective non cash food assistance recipients in accordance with established criteria, making it easier to filter, group, and rank appropriate population data according to criteria. In addition, this research is also useful for providing convenience to the community through data collection, clustering, and ranking in a transparent, real, and fast and accurate manner using decision support system software. The K-Means clustering method and the Simple Additive Weighting Ranking method were used in this study with data collection techniques through interviewing sources, in this case the village government, the social section of the community, and through collecting village archive data and relevant journals. The research location is Air Talas village with 316 data used. The results of the study are clustering data as much as 77 data obtained from feasible clusters. The cluster data was then tested using the accuracy value and obtained a value of 80%. Then the research is also in the form of ranking data using clustered data which obtains an accuracy value of 64%.
Co-Authors Allsela Meiriza, Allsela Angga Adiningrat Mulyanata Desty Rodiah Dicky Ahmad Rizaldi Evita Hardanita Fadhlan Jiwa Hanuraga Jerry Satiamy Saputra Kanda Januar Miraswan Kartika Rahmayani Luh Sri Mulia Eni M. Fachrurrozi . Muhammad Arbi Muhammad Rizkiansyah Novi Yusliani Oktadini, Nabila Rizky Osvari Arsalan Risky Tama Putri Rizki Kurniati Rusdi Efendi Wenty Octaviani