ANALYSIS OF E-OFFICE SYSTEM USER SATISFACTION AT LAND OFFICE PEKANBARU CITY USING END USER COMPUTING SATISFACTION METHOD

An E-Office system is available at the National Land Agency, which is part of the Ministry of Agrarian Affairs and Spatial Planning and the National Land Agency. It is designed to make office administration easier. However, the use of E-Office is still not optimally used, such as the minutes feature that is underused, errors in sending letters that are sent not as desired, the dashboard display is less attractive and the guide menu cannot be accessed because of a link error. The purpose of this study is to evaluate user satisfaction with the E-Office system and identify its results. The End User Computing Satisfaction (EUCS) method with six variables (content, accuracy, format, ease of use, timeliness, user satisfaction) was used by researchers to assess user satisfaction with the E-Office system. 37 individuals from the E-Office system user population comprised the sample. The results of the proposed hypothesis test show that the hypothesis is accepted and has a significant effect of 78.9% on user satisfaction

A Model to Predict The Live Bodyweight of Livestock Using Back-Propagation Algorithm

Cattle is the most popular livestock in Indonesia. Assessments of the live bodyweight of cattle can be conducted through weighing or predicting. Weighing is an accurate method, but it is not efficient due to the prices of scales that most traditional farmers cannot afford. Prediction is a more affordable technique however occurrences of error remains high. To deal with this issue this research has created a model predicting the live bodyweight of cattle through Back-Propagation algorithm. There are four morphometric variables examined in this study: (1) body length; (2) withers height; (3) chest girth; and (4) hip width. Based on comparative results with conventional prediction methods, Schoorl Indonesia and Schoorl Denmark, showed that the method offered has a lower error. Rate of error is 60.54% lower than Schoorl Denmark and 53.95% lower than Schoorl Indonesia

IT GOVERNANCE AUDIT AT THE KAMPAR REGENCY LIBRARY AND ARCHIVES DEPARTMENT USING COBIT 2019 AND ITIL 4

Information Technology (IT) is a tool that plays an important role in helping improve the effectiveness and efficiency of a company's or organization's business processes. The Kampar Regency Library and Archives Service uses the Integrated Library System (INLIS) Lite to support operational, management, and decision-making functions in the library. However, the use of INLIS Lite has not been fully utilized properly, for this reason it is necessary to carry out an IT governance audit. The audit process aims to determine the extent of IT performance, human resources, and the level of IT maturity in the library. This study uses Control Objective for Information and Related Technology (COBIT) 2019 and Information Technology Infrastructure Library (ITIL) 4 to conduct audits. With the findings of the audit results obtained eight process domains, namely APO02 and APO012 domains are at level 1 (Performed) in the Largely Achieved category, APO09 and BAI08 domains are at level 2 (Managed) in the Largely Achieved category, domains APO013, BAI05 and MEA01 are in the Largely Achieved, and the APO07 domain is at level 4 (Predictable) and in the Fully Achieved category. In addition to the audit, an assessment of the capability level was also carried out using the Servqual Model and Importance Performance Analysis with the results of obtaining 2 criteria in quadrant A, 1 criterion in quadrant B, 2 criteria in quadrant C and 3 criteria in quadrant D. This study also provides recommendations for improvement using SWOT model approach refers to ITIL 4

MODEL PENANGGULANGAN KEMISKINAN BERBASIS WILAYAH MENGGUNAKAN FUZZY C-MEANS DAN SIMPLE MATCHING COEFFICIENT

Kemiskinan merupakan masalah yang kompleks yang dihadapi Indonesia. Usaha untuk menanggulangi kemiskinan sudah lama diupayakan, namun hasilnya belum maksimal. Kemiskinan masih banyak dialami oleh rakyat dan terkonsentrasi pada wilayah-wilayah tertentu. Ada beberapa hal yang menyebabkan terjadinya konsentrasi kemiskinan tersebut, antara lain adanya perbedaan kemajuan pembangunan suatu wilayah yang dapat menimbulkan kesenjangan pendapatan dan sekaligus akan menimbulkan perbedaan tingkat kemiskinan. Pada penelitian ini dilakukan pengelompokkan kemiskinan menggunakan fuzzy clustering means. Dengan menggunakan 28 indikator kemiskinan dan dikelompokkan menjadi 5 kriteria (kesejateraan, pendidikan, lingkungan ekonomi, infrastruktur dan pelayanan, dan lingkungan pendukung) maka tingkat kemiskinan dapat dibagi menjadi 4 cluster wilayah kemiskinan yaitu sangat miskin, cukup miskin, agak miskin dan tidak miskin. Penanganan setiap wilayah kemiskinan satu daerah dengan daerah lain akan berbeda, tergantung dengan latar belakang kemiskinan wilayah tersebut. Dengan menggunakan case base reasoning dan model similarity matching coefficient maka langkah penanggulanggalan kemiskinan dapat disesuaikan dengan kondisi dan latar belakang wilayah tersebut

ANALISIS SENTIMEN CYBERBULLYING PADA KOMENTAR INSTAGRAM DENGAN MENGGUNAKAN METODE ALGORITMA NA¨IVE BAYES DAN K-NEAREST NEIGHBOR

Tingginya jumlah pengguna media sosial menghadirkan ancaman dan risiko yang besar, seperti perundungan siber (cyberbullying). Cyberbullying atau perundungan siber merupakan salah satu dampak negatif dari pesatnya perkembangan teknologi serta sosial media. Analisis Sentimen adalah suatu teknik mengekstrak data teks untuk mendapatkan informasi tentang sentimen bernilai positif, netral maupun negatif. Salah satu media sosial Indonesia yang sering mendapatkan sentimen pengguna melalui media sosial adalah Instagram. Dengan menggunakan teknik Text Mining metode klasifikasi, akan diketahui suatu sentimen bernilai positif atau negatif. Penelitian ini menggunakan metode Naïve Bayes Classifier (NBC) dan K-Nearest Neighbor (KNN) dengan pembobotan TF-IDF disertai penambahan fitur konversi ikon emosi (emoticon) untuk mengetahui kelas sentimen yang ada dari tweet tentang pengguna instagram. Hasil perhitungan dengan menggunakan ketiga metode yang pertama dengan menggunakan model Partitionong hasil dengan metode NBC mendapatkan nilai accuracy 91.25%, nilai recall 92% dan nilai precision 90% dan perhitungan dengan metode KNN nilai Accuracy 67%, nilai recall 49% dan nilai precision 34%. Sehingga dapat disimpulkan Algoritma metode Naïve Bayes Classifier merupakan performa terbaik

IT GOVERNANCE AUDIT AT THE KAMPAR REGENCY LIBRARY AND ARCHIVES DEPARTMENT USING COBIT 2019 AND ITIL 4

Information Technology (IT) is a tool that plays an important role in helping improve the effectiveness and efficiency of a company's or organization's business processes. The Kampar Regency Library and Archives Service uses the Integrated Library System (INLIS) Lite to support operational, management, and decision�making functions in the library. However, the use of INLIS Lite has not been fully utilized properly, for this reason it is necessary to carry out an IT governance audit. The audit process aims to determine the extent of IT performance, human resources, and the level of IT maturity in the library. This study uses Control Objective for Information and Related Technology (COBIT) 2019 and Information Technology Infrastructure Library (ITIL) 4 to conduct audits. With the findings of the audit results obtained eight process domains, namely APO02 and APO012 domains are at level 1 (Performed) in the Largely Achieved category, APO09 and BAI08 domains are at level 2 (Managed) in the Largely Achieved category, domains APO013, BAI05 and MEA01 are in the Largely Achieved, and the APO07 domain is at level 4 (Predictable) and in the Fully Achieved category. In addition to the audit, an assessment of the capability level was also carried out using the Servqual Model and Importance Performance Analysis with the results of obtaining 2 criteria in quadrant A, 1 criterion in quadrant B, 2 criteria in quadrant C and 3 criteria in quadrant D. This study also provides recommendations for improvement using SWOT model approach refers to ITIL 4