Analisis Sentimen merupakan cabang dari penelitian text mining yang melakukan proses pengklasifikasian dokumen teks. Analisis sentimen dapat melakukan ekstraksi pendapat, emosi, dan evaluasi tertulis seseorang tentang topik tertentu menggunakan teknik pemrosesan Bahasa alami. Pada penelitian ini melakukan analisis sentiment terhadap penggunaan aplikasi Shopee menggunakan algoritma Support Vector Machine (SVM). Tujuan dari penelitian ini adalah untuk mengklasifikasi data komentar dari pengguna aplikasi Shopee kedalam komentar positif dan negatif dengan mempelajari pendapat pengguna tentang aplikasi Shopee melalui ulasan yang diberikan, dan untuk mengetahui kinerja dari metode pengklasifikasi yang digunakan. Pada penelitian ini data diperoleh dengan cara mengangkat data dari ulasan penggunakan aplikasi Shopee menggunakan metode scraping dan berhasil mendapat 3000 data ulasan. Hasil penelitian menggunakan algoritma Support Vector Machine terbukti mampu menghasilkan kinerja yang cukup baik dengan hasil akurasi sebesar 98% dan f1-score sebesar 0.98 atau sebesar 98%.Sentiment analysis is a branch of text mining research that carries out the process of classifying text documents. Sentiment analysis can extract one's opinions, emotions, and evaluations about a certain topic using natural language techniques. In this study, sentiment analysis was carried out on the use of the Shopee application using the Support Vector Machine (SVM) algorithm. The purpose of this study is to classify comment data from Shopee application users, positive and negative comments by studying user opinions about the Shopee application through the reviews provided, and to determine the performance of the classifier method used. In this study, the data was obtained by collecting data from reviews on the use of the Shopee application using the scraping method and managed to get 3000 data reviews. The results of research using the Support Vector Machine algorithm are proven to be able to produce quite good performance with an accuracy of 98% and an f1-score of 0.98 or 98%.

Breast Cancer is the most common cancer found in women and the death rate is still in second place among other cancers. The high accuracy of the machine learning approach that has been proposed by related studies is often achieved. However, without efficient pre-processing, the model of Breast Cancer prediction that was proposed is still in question. Therefore, this research objective to improve the accuracy of machine learning methods through pre-processing: Missing Value Replacement, Data Transformation, Smoothing Noisy Data, Feature Selection / Attribute Weighting, Data Validation, and Unbalanced Class Reduction which is more efficient for Breast Cancer prediction. The results of this study propose several approaches: C4.5 - Z-Score - Genetic Algorithm for Breast Cancer Dataset with 77,27% accuracy, 7-Nearest Neighbor - Min-Max Normalization - Particle Swarm Optimization for Wisconsin Breast Cancer Dataset - Original with 97,85% accuracy, Artificial Neural Network - Z-Score - Forward Selection for Wisconsin Breast Cancer Dataset - Diagnostics with 98,24% accuracy, and 11-Nearest Neighbor - Min-Max Normalization - Particle Swarm Optimization for Wisconsin Breast Cancer Dataset - Prognostic with 83,33% accuracy. The performance of these approaches is better than standard/normal machine learning methods and the proposed methods by the best of previous related studies.

Penyakit Dypepsia merupakan kumpulan keluhan/gejala klinis (sindrom) rasa tidak nyaman atau nyeri yang di rasakan di daerah abdomen bagian atas yang disertai dengan keluhan lain yaitu perasaan panas di dada dan perut, regurgitas, kembung, perut terasa penuh, cepat kenyang, sendawa, anoreksia, mual, muntah dan banyak mengeluarkan gas dari mulut. Data Medical Record RSUD Prof. Dr. H. Aloei Saboe Kota Gorontalo dari tahun 2013-2015, jumlah pasien penyakit Dyspepsia mencapai 2.666 orang, rata-rata setiap tahunnya ada sekitar kurang lebih 600 pasien penyakit Dyspepsia di rawat inap dan rawat jalan. Metode Bayes merupakan metode yang baik didalam mesin pembelajaran berdasarkan data training, dengan menggunakan probabilitas bersyarat sebagai dasarnya. Metode Bayes juga merupakan suatu metode untuk menghasilkan estimasi parameter dengan menggabungkan informasi dari sampel dan informasi lain yang telah tersedia sebelumnya. implementasi Sistem Diagnosis Penyakit Dyspepsia Menggunakan Metode Bayes dapat direkayasa, sehingga membantu dan memudahkan pihak RSUD Prof.Dr.H. Aloei Saboe Kota Gorontalo serta pihak masyarakat dalam mendiagnosa penyakit Dyspepsia.

City Park is part of the city's green open space, its existence has the meaning of securing natural ecosystems that have a great influence on the existence and survival of the city itself. The number of city parks in the city of Gorontalo also requires little cleaning and maintenance personnel. To find out the performance of officers in the field, we need a system that is able to monitor the results of the work, making it easier for the relevant offices to control the conditions, facilities and functions of the city park. This research is intended to build a geographic information system that will be used for monitoring park facilities equipped with related information that is easily accessible by the local government, especially the City Planning and Landscaping Office of Gorontalo City. This study uses the programming language PHP (PHP: Hypertext Preprocessor) and MySQL database, using descriptive methods, then implement this design to find out and measure the level of ease, speed of information, and accuracy of information. The results of the study based on the data obtained were then tested using the White Box Testing method and Bases Path Testing. From the data obtained then a flowgraph design was made. Flowgraph that is tested is the process of finding a location of a garden. From the results of the calculation of the White Box Testing and Bases Path Testing test methods, the results of the calculation results obtained that have met the requirements in terms of software feasibility. Based on the results of testing with the White Box Testing, and Base Path Testing method above, it can be concluded that true system logic can produce a system that is effective and efficiently logically, and is expected to facilitate the processing of said data.

Kesalahan pengetikan merupakan hal yang biasa terjadi ketika membuat tulisan, misalnya  ketika membuat karya ilmiah, buku maupun lainnya. Kesalahan penulisan kata memang hal yang biasa terjadi tetapi dapat berakibat buruk sehingga perlu dilakukan pemeriksaan kata terhadap tulisan pada dokumen yang dibuat. Typo checking merupakan proses pemeriksaan kata yang dilakukan untuk mendeteksi kesalahan penulisan kata dan memberikan kandidat kata yang benar.  Pemeriksaan kesalahan penulisan membutuhkan waktu lama jika dilakukan secara manual, sehingga dibuat aplikasi untuk mendeteksi kesalahan penulisan kata menggunakan Algoritma Rabin-Karp, dengan mencocokkan string berdasarkan nilai hash pada teks dan pattern. Proses Pengecekan Penulisan Kata dilakukan dengan menghitung sampai indeks akhir dan didapatkan hasil seperti kata dan nilai hash. Proses hashing menggunakan modulo (sisa bagi) sebesar 107 dengan nilai k-gram k=3 pada setiap kata asal dan kata hasil. Proses hashing dilakukan dengan cara mengkonversi string menjadi nilai ASCII, sehingga diperoleh nilai hash a-z = 79-122. Berdasarkan hasil perhitungan manual yang telah dilakukan, jika terdapat kesalahan pengetikan akan diperoleh nilai hashing yang berbeda antara kata asal dan kata yang dihasilkan.   Typing errors are common when writing, for example, when writing scientific papers, books, and others. Word writing errors are common but can have bad consequences, so it is necessary to check the words on the writing in the document that is made. Typo checking is a word checking process that is carried out to detect word writing errors and provide the correct word candidate. Checking writing errors takes a long time if done manually, so an application is made to detect word writing errors using the Rabin-Karp Algorithm, by matching strings based on hash values in text and patterns. The process of Checking Word Writing is done by counting to the final index and getting results such as words and hash values. The hashing process uses a modulo (remaining for) of 107 with a value of k-gram k=3 for each word of origin and word of the result. The hashing process is done by converting the string into an ASCII value so that the hash value a-z = 79-122. Based on the results of manual calculations that have been carried out, if there are typing errors, a different hashing value will be obtained between the original word and the resulting word.