Prediksi Penambahan Piutang Iuran Jaminan Sosial Ketenagakerjaan menggunakan Algoritma K-Nearest Neighbor

There are several issues with Social Security Organizing Agency (BPJS) employment at the moment, one of which is contribution receivable. To reduce the BPJS contribution receivables, BPJS has done various ways. However, the resulting effort is not maximal enough to reduce the number of receivables in BPJS. This study aims to provide input by predicting the addition of receivables from social security contributions made by several companies or organizations. This study used the K-Nearest Neighbor (KNN) Algorithm with a cross-validation technique. KNN is a very simple classification method in classifying an image based on the closest distance to its neighbors. This study conducted data processing from BPJS use, which amounted to 1193 data. The data is then preprocessed so that the processed data is clean from missing and noise, this data uses 70:30 data splitting. After the preprocessing and splitting of data were carried out, the next step was to do modeling using KNN, so the cross-validation to improve the accuracy of results obtained from the KNN algorithm. The results obtained from this research get the highest accuracy of 92% with the Optimal K value being 6, then the ROC curve gets 94% accuracy. From these results, it can be said that the use of cross-validation can increase the accuracy of this study

Sistem Pendukung Keputusan Penunjukan Supplier Pengadaan Perangkat Kesehatan Pada Instalasi Farmasi RSUD Arifin Achmad Pekanbaru Dengan Metode Multifactor Evaluation Process

RSUD Arifin Ahcmad Pekanbaru setiap harinya membutuhkan berbagai macam perangkat kesehatan guna menunjang keperluan medis di rumah sakit tersebut, dan perangkat kesehatan tersebut dipasok oleh supplier yang ditunjuk. Selama ini dalam proses penunjukan supplier masih mengunakan proses manual yang berubah-ubah dan dikhawatirkan akan berdampak kurang baik terhadap kelancaran suplai hingga kualitas perangkat kesehatan yang disuplai, disisi lain tanpa didukung sistem informasi yang handal, akan sulit bagi berbagai  unsur  terkait  untuk  melakukan  antisipasi maupun  perencanaan  dan  pengembangan  untuk jangka  panjang (Edwar Ali, Susandri & Rahmaddeni, 2015). Untuk itu perlu dibuat sebuah sistem yang nantinya dapat menampilkan laporan kelayakan supplier berdasarkan kriteria yang dibutuhkan, kriteria yang dimaksud berupa : Harga, kualitas, kelengkapan dan garansi. Sistem pendukung keputusan ini dibuat dengan menggunakan bahasa pemograman PHP dan database MYSQL. Metode MFEP digunakan dengan memberikan pertimbangan subyektif dan intuitif terhadap Faktor yang dianggap penting. Pertimbangan-pertimbangan tersebut berupa pemberian bobot (weighting system) atas multifactor yang terlibat dan dianggap penting tersebut. Dengan menggunakan sistem ini diharapkan pihak RSUD Arifin Ahcmad akan memperoleh kemudahan dalam mendapatkan informasi yang dibutuhkan yang berkaitan dengan kegiatan pengadaan perangkat kesehatan di RSUD Arifin Ahcmad

Sistem Prediksi Keuntungan Influencer Pengguna E-Commerce Shopee Affiliates menggunakan Metode Naïve Bayes

Shopee Affiliate is one of Shopee's e-commerce programs to make it easier to market products. However, with the popularity of this program, there are still many people who do not know the advantages of this program. As a result, in this e-commerce, not all sellers benefit (loss) from the products sold. In order to avoid the problem of losses on marketed products, this study aims to produce a profit prediction system for shoppe affiliate e-commerce users. To build the system, this research uses the waterfall method which is used to complete the prediction system. The first stage is to collect data from social media and references related to the prediction system, then design a prediction system, after carrying out the process of system creation and implementation and testing. The test uses blackbox to test the system and accuracy test to determine the level of accuracy of this system. The result of this prediction system is to gain knowledge in the form of profit rate patterns of influencers of shopee affiliate e-commerce users. Testing the accuracy of the system built has a very good performance with a percentage of 100%. So that the profit prediction of shopee affiliate e-commerce users is feasible to be implemented. With this system, it is hoped that the community will be able to increase sales at e-commerce shopee

Sistem Pendeteksi Tingkat Kesamaan Teks pada Pengusulan Proposal Penelitian Internal Menggunakan Algoritma Rabin-Karp

STMIK Amik Riau yang merupakan perguruan tinggi komputer di Provinsi Riau memiliki suatu unit lembaga yang mengelola penelitian yang bernama LPPM STMIK Amik Riau. Untuk mewujudkan visi LPPM STMIK Amik Riau, lembaga ini memulainya dari hal internal terlebih dahulu berupa adanya pengelolaan manajemen penelitian yang baik dalam sebuah website yang dapat diakses secara online. Permasalahan yang terjadi selama ini dalam sebuah website LPPM STMIK Amik Riau adalah dalam hal pengajuan proposal penelitian internal. Dalam pengajuan proposal internal yang dilakukan oleh dosen STMIK Amik Riau melalui sistem yang ada, belum bisa mendeteksi adanya kesamaan teks yang diajukan melalui paparan abstrak yang diberikan. Sistem yang ada menerima semua usulan yang diberikan dalam hal abstrak tanpa memberikan rekomendasi ke reviewer akan tingkat kesamaan usulan penelitian yang diajukan dengan penelitian yang ada. Menanggapi permasalahan yang ada dan untuk mewujudkan visi LPPM STMIK Amik Riau, maka perlu adanya sistem yang mampu memberikan rekomendasi bagi reviewer akan tingkat kesamaan teks melalui usulan abstrak penelitian yang diajukan oleh dosen STMIK Amik Riau. Tujuan dari penelitian ini adalah menerapkan algoritma Rabin-Karp pada website LPPM STMIK Amik Riau untuk mendeteksi tingkat kesamaan abstrak penelitian internal STMIK Amik Riau agar meminimalisir terjadinya penelitian yang plagiat. Selain berbasis website, sistem juga dapat diakses melalui perangkat mobile android

Analisis Kualitas Sistem Omnichannel pada PT. BFI Menggunakan Model ISO 25010

Omnichannel merupakan suatu transformasi dalam budaya organisasi, operasi dan proses, dan teknologi. Pada tahun 2021 PT BFI Finance mulai menggunakan platform Omnichannel dalam melakukan otomasi reminder (pengingat) angsuran pinjaman nasabah sebelum jatuh tempo. Fitur omnichannel memiliki fungsi yang beragam dengan salah satu fiturnya adalah pengiriman otomatis whatsapp dan sms. Penelitian ini bertujuan untuk menganalisis kualitas platform Omnichannel pada PT BFI menggunakan metode ISO 25010. Metode penelitian yang digunakan adalah deskriptif. Pengujian ISO 25010 merupakan standar internasional yang digunakan dalam menganalisis kualitas dari suatu perangkat lunak. Penelitian ini membahas mengenai analisis kualitas aplikasi menggunakan ISO 25010 dengan lima karakteristik yaitu Functional Suitability, Usability, Performance Efficiency, Portability and Compatibility. Hasil dari karakteristik functional suitability yaitu platform omnichannel dikatakan baik. Usability mendapatkan 76,67 % yang berarti platform omnichannel disebut layak. Performance efficiency website termasuk baik karena proses load kurang dari 10 detik. Dari hasil tersebut disimpulkan bahwa platform Omnichannel memenuhi predikat puas. Compatibility platform Omnichannel tidak menemukan masalah letak dan performance pada browser Edge, Chrome dan Android. Hasil penilaian diharapkan dapat menjadi rekomendasi dan saran untuk melakukan pengembangan platform omnichannel dalam membantu proses pengiriman digital reminder yang lebih baik dan efisien

Data Mapping System Of Riau Province Fire Potential Using K-Means Clustering Method

According to a report from the Riau Province BLHK states that hotspots in Riau Province are always present every year despite the number of hotspots that have been suppressed (http://dislhk.riau.go.id/). One of the causes is the frequent land clearing occurred as a trigger from a hotspot in Riau Province. There is a need for countermeasures as soon as possible to overcome the problem of hotspots that will cause forest fires. These problems need to be watched out quickly, one of which is to know in advance the hotspots that are likely to emerge based on existing data. Data mining processing is very suitable to be applied in order to produce relevant data to find out the possibility of hotspots. In this study the data grouping was done in the form of a visualization of hotspot mapping using the K-means Clustering method. The parameters used include 3 number of clusters (critical, alert, vigilant), 12 regencies / cities in Riau Province and 3 attributes (hotspots, number of fires, number of events). With the results of the visualization of the mapping using the K-means Clustering method, it is expected to be able to help the relevant parties, namely the Riau Provincial Forest Service in handling early the hotspots that are likely to emerge

Sentiment Analysis of Technology Utilization by Pekanbaru City Government Based on Community Interaction in Social Media

Government services for the public are currently utilizing technology, especially in the city of Pekanbaru. The government has currently centralized all services for the public, both online and offline, in public service malls. The type of service that uses technology, especially for online services, has received criticism in online media such as Twitter. To see the public's response to Pekanbaru city government services, especially in terms of technology, this study will use sentiment analysis to see positive, negative, and neutral comments. The method used is to see the accuracy generated using the Naïve Bayes Classifier (NBC) method. Bayes classifier is a statistical classifier, where the classifier can predict the probability of class membership of a data tuple that will fall into a certain class, according to the probability calculation. Accuracy results are obtained by dividing training data and testing data with a comparison of 70%:30% with an accuracy value of 55.56%, Precision 64%, recall 80%, f-score 71.2%

Penerapan Metode Simple Additive Weighting (SAW) Dalam Penentuan Siswa Berprestasi Tingkat Sekolah Dasar

Pemberian penghargaan terhadap siswa berprestasi di SDN Pondok Bahar 02 sangat penting karna dapat memotivasi prestasi dan meningkatkan antusiasme siswa dalam belajar. Penentuan siswa berprestasi yang dilakukan oleh SD Negeri Pondok Bahar 02 hanya diambil dari rata-rata semua nilai dari ketiga aspek yaitu keterampilan, pengetahuan dan sikap. Hal tersebut dianggap kurang tepat karena siswa berprestasi harus aktif dalam kegiatan sekolah, memiliki kepribadian yang bagus dan beberapa kriteria lainya sehingga proses penentuan siswa berprestrasi tersebut kurang akurat. Selain itu, Penentuan siswa berprestasi dengan beberapa kriteria akan memerlukan banyak waktu untuk menghitungnya.  Untuk itu membutuhkan sistem yang dapat menangani manajemen pengambil keputusan dalam menentukan siswa berprestasi secara cepat dan akurat. Metode Simple Simple Additive Weighting (SAW) adalah metode yang dapat digunakan untuk menentukan siswa berprestasi di SDN Pondok Bahar 02. Ada lima kriteria yang akan digunakan sebagai referensi untuk menentukan prestasi belajar siswa yaitu nilai rata-rata jumlah raport semester 2, rata-rata jumlah pengetahuan, rata-rata jumlah keteramilan, rata-rata jumlah sikap dan absensi (Alfa). Nilai terbesar akan menghasilkan siswa yang berkualitas. Dengan sistem ini, sekolah dapat dengan mudah, cepat dan akurat dalam memilih kriteria dan menentukan siswa beprestasi

Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA)

The Social Security Administrative Body is a legal entity established to administer social security programs. News about BPJS policies is often found online and social media that has received responses from netizens as a form of public opinion on the policy. One of them is the opinion of netizens on social media Twitter. Ideas can be positive, neutral, or negative. These opinions are processed using the Support Vector Machine (SVM) method, in some SVM studies still getting unsatisfactory results, with rates below 60%. For this reason, it is necessary to have feature selection or a combination with the other methods to obtain higher accuracy. To see the actors who influence the opinion of netizens on the topic of BPJS, the Social Network Analysis (SNA) method is used. Based on the SVM Method's test results, the best accuracy results are obtained in combining the SVM Method with Adaboost, with an accuracy rate of 92%. Compared to the pure SVM method by 91%, the Combination of SVM Particle Swarm Optimization (PSO) by 87% and SVM using Feature Selection Genetic Algorithm (GA) by 86%.The Social Security Administrative Body is a legal entity established to administer social security programs. News about BPJS policies is often found online and social media that has received responses from netizens as a form of public opinion on the policy. One of them is the opinion of netizens on social media Twitter. Ideas can be positive, neutral, or negative. These opinions are processed using the Support Vector Machine (SVM) method, in some SVM studies still getting unsatisfactory results, with rates below 60%. For this reason, it is necessary to have feature selection or a combination with the other methods to obtain higher accuracy. To see the actors who influence the opinion of netizens on the topic of BPJS, the Social Network Analysis (SNA) method is used. Based on the SVM Method's test results, the best accuracy results are obtained in combining the SVM Method with Adaboost, with an accuracy rate of 92%. Compared to the pure SVM method by 91%, the Combination of SVM Particle Swarm Optimization (PSO) by 87% and SVM using Feature Selection Genetic Algorithm (GA) by 86%