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Enhancement of K-Parameter Using Hybrid Stratified Sampling and Genetic Algorithm Ramadhani, Rima Dias; Priyanto, Agus; Sidiq, Muhammad Fajar
JURNAL INFOTEL Vol 10 No 1 (2018): February 2018
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Clustering is a technique used to classify data into clusters based on their similarities. K-means is a clustering algorithm method that classifies the objects based on their closest distance to the cluster center to the groups that have most similarities among the members. In addition, K-means is also the most widely used clustering algorithm due to its ease of implementation. However, the process of selecting the centroid on K-means still randomly. This results K-means is often trapped in local minimum conditions. Genetic algorithm is used in this research as a metaheuristic method where the algorithm can support K-means in reaching global optimum function. Besides, the stratified sampling is also used in this research, where the sampling functions by dividing the population into homogeneous areas using stratification variables. The validation value of the proposed method with iris dataset is 0.417, while the K-means is only 0.662.

IMPLEMENTASI METODE SIMPLE ADDITIVE WEIGHTING (SAW) DAN WEIGHTED PRODUCT (WP) DALAM PEMILIHAN GURU TELADAN (STUDI K ) Aulia, Avina Ulfa; Supriyadi, Didi; Ramadhani, Rima Dias
Proceeding SENDI_U 2018: SEMINAR NASIONAL MULTI DISIPLIN ILMU DAN CALL FOR PAPERS
Publisher : Proceeding SENDI_U

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (588.311 KB)

Guru menjadi salah satu komponen terpenting yang dimiliki oleh sekolah dalam usaha untuk meningkatkan layanan pendidikan. Oleh karena itu guru harus senantiasa di motivasi agar memberikan yang terbaik kepada siswa dan siswinya. Sehingga sekolah tersebut dapat meningkatkan kualitas sekolahnya hingga tumbuh menjadi sekolah yang besar. Salah satu upaya dalam meningkatkan kualitas sekolah yaitu dengan melakukan pengukuran 1 Ajibarang Wetan dilakukan dengan cara manual yang memilih salah satu guru yang direkomendasikan oleh guru-guru lain. Kendala yang lain yang timbul dalam pemilihan guru teladan yaitu belum adanya kriteria terukur yang digunakan untuk menentukan pemilihan guru teladan. Oleh sebab itu dibutuhkan suatu sistem pengambilan pendukung keputusan ini menggunakan metode Simple Additive Weighting (SAW), dimana dalam mencari penjumlahan terbobot dari suatu rating kinerja pada setiap alternatif yang terdapat pada semua atribut. Serta menggunakan metode Weighted Product (WP), dimana konsep dasar metode ini menggunakan perkalian untuk menghubungkan rating atribut, dimana pada setiap atribut terlebih dahulu dipangkatkan dengan bobot atribut yang bersangkutan. Hasil penelitian ini dengan menggunakan metode SAW dan WP menunjukan A1 memliki peringkat teratas yaitu Muliyah, S.Pd.I dengan nilai 99,5748 dan 0,1440.

IMPLEMENTASI METODE SIMPLE ADDITIVE WEIGHTING (SAW) DAN WEIGHTED PRODUCT (WP) DALAM PEMILIHAN GURU TELADAN (STUDI K ) Aulia, Avina Ulfa; Supriyadi, Didi; Ramadhani, Rima Dias
Proceeding SENDI_U 2018: SEMINAR NASIONAL MULTI DISIPLIN ILMU DAN CALL FOR PAPERS
Publisher : Proceeding SENDI_U

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (588.311 KB)

Guru menjadi salah satu komponen terpenting yang dimiliki oleh sekolah dalam usaha untuk meningkatkan layanan pendidikan. Oleh karena itu guru harus senantiasa di motivasi agar memberikan yang terbaik kepada siswa dan siswinya. Sehingga sekolah tersebut dapat meningkatkan kualitas sekolahnya hingga tumbuh menjadi sekolah yang besar. Salah satu upaya dalam meningkatkan kualitas sekolah yaitu dengan melakukan pengukuran 1 Ajibarang Wetan dilakukan dengan cara manual yang memilih salah satu guru yang direkomendasikan oleh guru-guru lain. Kendala yang lain yang timbul dalam pemilihan guru teladan yaitu belum adanya kriteria terukur yang digunakan untuk menentukan pemilihan guru teladan. Oleh sebab itu dibutuhkan suatu sistem pengambilan pendukung keputusan ini menggunakan metode Simple Additive Weighting (SAW), dimana dalam mencari penjumlahan terbobot dari suatu rating kinerja pada setiap alternatif yang terdapat pada semua atribut. Serta menggunakan metode Weighted Product (WP), dimana konsep dasar metode ini menggunakan perkalian untuk menghubungkan rating atribut, dimana pada setiap atribut terlebih dahulu dipangkatkan dengan bobot atribut yang bersangkutan. Hasil penelitian ini dengan menggunakan metode SAW dan WP menunjukan A1 memliki peringkat teratas yaitu Muliyah, S.Pd.I dengan nilai 99,5748 dan 0,1440.

Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah Rima Dias Ramadhani; Afandi Nur Aziz Thohari; Condro Kartiko; Apri Junaidi; Tri Ginanjar Laksana; Novanda Alim Setya Nugraha
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.

Sistem Keamanan Ruangan Berbasis Internet of Things Menggunakan Single Board Computer Rima Dias Ramadhani; Afandi Nur Aziz Thohari; Novanda Alim Setya Nugraha
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 4, No 2 (2020): InfoTekJar Maret
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v4i2.2338

Closed Circuit Television (CCTV) is a security system to monitoring a room. In recent years, the use of CCTV is becoming less effective. CCTV usually have expensive rental fees and expensive device. Surveillance system using CCTV still need security officer to monitoring room condition through TV Screen. In this research purposed to build surveillance system using artificial intelligence method. The system features are detect object and send notification through Short Message Service (SMS). Single Board Computer (SBC) is used to processing video data. Technique for detecting objects is Structural Similarity (SSIM). Thought this technique, system have more accuration because it can't read shadow as object. Based on testing result obtained that system can detect object and send notification to user through SMS. System can't read object if low light intensity, but if high intensity of light the system can detect objects that have far position. Maximum frame rate that used to capture video is 60 fps, because limitation of SBC that used.

Analisis Sentimen Masyarakat Terhadap COVID-19 Pada Media Sosial Twitter Ardianne Luthfika Fairuz; Rima Dias Ramadhani; Nia Annisa Ferani Tanjung
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 1 No 1 (2021): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Akhir tahun 2019 lalu dunia digemparkan oleh munculnya suatu penyakit yang disebabkan oleh virus SARS-CoV-2 yang merupakan jenis virus terbaru dari coronavirus. Penyakit ini dikenal dengan nama COVID-19. Penyebaran penyakit ini terbilang cukup luas dan cepat. Dalam waktu singkat penyakit ini mulai menyebar ke segala penjuru dunia tak terkecuali Indonesia. Dengan tingkat penyebaran yang begitu tinggi dan belum ditemukannya vaksin untuk COVID-19, menyebabkan kekacauan di tengah masyarakat. Hal ini mempengaruhi banyak sektor kehidupan masyarakat. Tak sedikit masyarakat yang aktif bersosial media dan menuliskan pendapat, opini serta pemikirannya di platform media sosial seperti Twitter. Terjadinya pandemi ini mendorong masyarakat untuk menuliskan opini, pemikiran serta pendapatnya terhadap COVID-19 pada media sosial Twitter. Dibutuhkan suatu model sentiment analysis untuk mengklasifikasi tweet masyarakat di Twitter menjadi positif dan negatif. Sentiment analysis merupakan bagian dari Natural Language Processing yang membuat sebuah sistem guna mengenali serta mengekstraksi opini dalam bentuk teks. Pada penelitian ini digunakan algoritma Naive Bayes dan K-Nearest Neighbor untuk digunakan dalam membangun model sentiment analysis terhadap tweet pengguna Twitter terhadap COVID-19. Didapatkan akurasi sebesar 85% untuk algoritma Naïve Bayes dan 82% untuk algoritma K-Nearest Neighbor pada nilai k=6, 8, dan 14.

Perbandingan Performa Antara Algoritma Naive Bayes Dan K-Nearest Neighbour Pada Klasifikasi Kanker Payudara Annisa Nugraheni; Rima Dias Ramadhani; Amalia Beladinna Arifa; Agi Prasetiadi
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 2 No 1 (2022): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v2i1.391

Breast cancer is the second most common cause of death from cancer after lung cancer is in the first place. Breast cancer occurs when cells in breast tissue begin to grow uncontrollably and can disrupt existing healthy tissue. Therefore, there is a need for a classification to distinguish breast cancer patients and healthy people. Based on previous research, the Naïve Bayes and K-Nearest Neighbor algorithms are considered capable of classifying breast cancer. In the research process using the breast cancer dataset from the Breast Cancer Coimbra dataset in 2018 UCI Machine Learning Repository with a total of 116 data, while for the calculation of the feasibility of the method using the Confusion Matrix (Accuracy, Precision, and Recall) and the ROC-AUC curve. The purpose of this study is to compare the performance of the Naïve Bayes and K-Nearest Neighbor algorithms. In testing using the Naïve Bayes algorithm and the K-Nearest Neighbor algorithm, there are several test scenarios, namely, data testing before and after normalization, model testing based on a comparison of training data and testing data, model testing based on K values in K-Nearest Neighbors, and model testing. based on the selection of the strongest attribute with the Pearson correlation test. The results of this study indicate that the Naïve Bayes algorithm has the highest average accuracy of 69.12%, healthy precision 64.90%, pain precision 83%, healthy recall 88%, sick recall 61.11% and AUC 0.82 which is included in the good classification category. Meanwhile, the highest average results of the K-Nearest Neighbor algorithm are 76.83% for accuracy, 76% healthy precision, 80.21% pain precision, 74.18% for healthy recall, 80.81% sick recall and 0.91 AUC which is included in the excellent classification category.

Big Data Analytics to Analyze Sentiment, Emotions, and Perceptions of Travelers (Case Study: Tourism Destination in Purwokerto Indonesia) Siti Khomsah; Rima Dias Ramadhani; Sena Wijayanto
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 5 No 2 (2021)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v5i2.791

Big data analytics can extract travelers' sentiment, emotions, and experiences from their internet opinions. This study analyzes sentiment, emotion, and traveler experiences at eight tourism destinations in Purwokerto Central Java, Indonesia. The methods are lexicon using NCR vocabulary(EmoLex) and word cloud analysis. The results show visitors generally have a positive sentiment. The five destinations with high positive sentiment are the Village (91%), Lokawisata Baturaden(81%), Baturaden Forest (79%), Limpa Kuwus (78%), and Taman Andang(.77%). In comparison, other destinations achieve positive sentiment under 70%. Only a few visitors give negative sentiment to all tourism destinations. The emotion of visitors stands out in Joy and Trust. NRC revealed sadness dan anger emotion but only about 20%. Cloud analysis does not reveal a distinguish keyword because the word feature still contained noise such as conjunction, adverb, and the name of the sites. Further research must consider other text preprocessing to handle noises.

Prediksi Harga Saham Bank Bri Menggunakan Algoritma Linear Regresion Sebagai Strategi Jual Beli Saham Janur Syah Putra; Rima Dias Ramadhani; Auliya Burhanuddin
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 2 No 1 (2022): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v2i1.273

Shares are securities as proof of ownership of investors in a company. Stocks have a volatile nature, this makes stocks difficult to predict. Stock prediction is an effort to estimate the stock price, especially in the Bank Rakyat Indonesia company that will appear in the future, and to increase investors' profit opportunities in making investment decisions. During the COVID-19 pandemic, Bank BRI's shares experienced significant ups and downs in four months, which illustrates the sensitivity of the stock to an event. Therefore, it is important to predict stock prices to reduce the risk accepted by investors. The prediction itself requires time series data. Time series is data that is collected sequentially from time to time. The method used for time series data is Linear Regression because this method can handle time-series data. Based on these problems, stock prediction research will be conducted at the Bank Rakyat Indonesia company using the Linear Regression method. Bank Rakyat Indonesia share price data were obtained from the investing.com website from the period starting on January 1, 2008, to June 1, 2020. The data is processed starting from preprocessing to determine attributes, remove unnecessary attributes, and change the contents of the data type, then process split data to divide the dataset into training and test data. The attributes used in this study are Date and Price and the distribution of the data used is 60:40, 65:35, 70:30, 75:25, and 80:20. The best ratio is at 80:20 which produces train and test accuracy of 0.89 and 0.91, Then each training data and testing data are entered into the linear regression model for prediction. The error results from the predictions were calculated using MAPE and yielded a percentage of 13.751% for training data, 13.773% for test data, and 13.755% for overall data. The MAPE results indicate that the linear regression method can be used to predict the stock price of BRI Bank.

Perancangan Contingency Planning Disaster Recovery Unit Teknologi Informasi menggunakan NIST SP800-34 Wahyu Adi Prabowo; Rima Dias Ramadhani
Techno.Com Vol 20, No 1 (2021): Februari 2021
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v20i1.4114

Pembangunan institusi pendidikan selama ini telah bertumbuh pesat sesuai dengan kebutuhan masyarakat dan menjadikan sebuah insitusi yang semakin komplek dengan kebutuhan fungsi operasional sistem layanan informasinya. Untuk menjalankan fungsinya, institusi pendidikan didukung oleh infrastruktur sistem layanan teknologi informasi yang sangat kompleks. Dalam penyelenggaraan fungsi operasional layanan tersebut, perguruan tinggi membutuhkan peran sistem teknologi informasi yang handal dalam keberlangsungan kegiatan kerjanya. Semua komponen teknologi informasi merupakan komponen yang rentan terhadap gangguan baik itu dari internal maupun eksternal, untuk itu dalam penyelenggaraan institusi pendidikan, perguruan tinggi dalam hal ini wajib memiliki rencana untuk menanggulangi segala gangguan maupun bencana. Dalam hal ini penanganan penanggulangan ganguan dan bencana memuat beberapa prosedur dan mekanisme tersendiri dalam pengamanan datanya. Disaster Recovery Plan (DRP) merupakan langkah tepat dalam membangun penanganan gangguan dan bencana terhadap infrastruktur sistem layanan teknologi informasi yang ada di perguruan tinggi. Penerapan untuk membangun penanganan bencana ini mengacu pada NIST SP 800-34 Rev.1 yang didalamnya terdapat beberapa tahapan penilaian resiko, menganalisa dampak bisnis, mengidentifikasi pencegahannya dan pengembangan strategi mitigasi. Hasil akhir dari penelitian ini adalah rancangan dokumen DRP berdasarkan NIST SP 800-34 Rev.1 yang disesuaikan dengan kondisi di perguruan tinggi

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