Monitoring dan Kendali PC Melalui SMS Ponsel

Saat ini penggunaan PC (Personal Computer) semakin meluas dan banyak orang yang mampu mengoperasikannya. Seiring dengan hal tersebut, terdapat banyak sekali penyalahgunaan yang terjadi pada PC . Meskipun sudah ada hak akses untuk user tertentu, sering terjadi pemakaian PC tanpa izin user yang memiliki hak akses. Keamanan data yang tersimpan pada PC, bagi sebagian orang menjadi sangat penting. Yang yang masih dirasakan saat ini adalah pengendalian PC masih mengharuskan user untuk bertatap muka dengan PC tersebut. Telepon seluler dengan fasilitas SMS yang mampu bertukar informasi berbasis teks secara jarak jauh dan tanpa kabel (wireless) dapat digunakan untuk mendapatkan beragam informasi yang diinginkan. memiliki biaya operasional yang cukup murah,fasilitas ini juga merupakan media komunikasi dan sarana informasi antar individu yang memiliki sifat waktu nyata (real-time). Kelebihan fitur SMS tersebut dapat digunakan sebagai media pengendali dan monitoring perangkat lunak pada PC secara jarak jauh. Dengan adanya aplikasi tersebut, user yang memiliki hak akses dapat mengetahui dan memonitor aplikasi/file yang sedang dibuka oleh user lain yang diketahui melalui SMS. Selain itu user yang memiliki hak akses juga dapat melakukan tindakan pengendalian dengan memberi perintah melalui SMS. Pengendalian yang dapat dilakukan yaitu menutup windows yang sedang aktif ataupun melakukan shutdown jarak jauh. Dengan demikian kerja sebuah PC dapat kita monitoring selama PC dalam keadaan aktif/nyala

Pendekatan Data Science untuk Mengukur Empati Masyarakat terhadap Pandemi Menggunakan Analisis Sentimen dan Seleksi Fitur

Empati merupakan kemampuan seseorang untuk turut merasakan penderitaan orang lain. Pandemi covid yang melanda dunia, telah menyisakan banyak kehilangan dan keterpurukan. Penelitian ini bertujuan untuk mengetahui emosi masyarakat terhadap penderitaan sesama menggunakan pendekatan sentimen analisis. Dataset yang digunakan adalah komentar masyarakat di Twitter tentang pandemi Covid dalam rentang waktu November-Desember 2020. Data diambil dengan teknik crawling menggunakan library twint, didapatkan data sebanyak 2386 komentar, namun komentar yang mengandung empati hanya sebanyak 984 data. Dataset empati kemudian dilabeli oleh tiga orang menggunakan teknik majority voting. Hasil pengukuran dataset empati menunjukkan 55,7% komentar masyarakat indonesia mengandung empati positif (berempati), 37,4% empati negatif (tidak berempati), dan 6,9% netral. Untuk membentuk model yang dapat mendeteksi empati secara otomatis, maka digunakan  dataset empati sebanyak 400, dengan 200 kelas positif dan 200 kelas negatif, kelas netral tidak digunakan pada penelitian ini karena jumlah data sangat sedikit. Metode machine learning yang digunakan untuk membangun model adalah Support Vector Machine (SVM) dengan metode ekstraksi fitur reliefF. Berdasarkan penelitian yang dilakukan, akurasi sistem dengan metode SVM tanpa seleksi fitur ReliefF adalah 83%. Sedangkan akurasi yang diperoleh sistem dengan seleksi fitur ReliefF mencapai 93% dengan penggunaan 85% fitur dari total keseluruhan fitur

Music Emotion Classification based on Lyrics-Audio using Corpus based Emotion

Music has lyrics and audio. That’s components can be a feature for music emotion classification. Lyric features were extracted from text data and audio features were extracted from audio signal data.In the classification of emotions, emotion corpus is required for lyrical feature extraction. Corpus Based Emotion (CBE) succeed to increase the value of F-Measure for emotion classification on text documents. The music document has an unstructured format compared with the article text document. So it requires good preprocessing and conversion process before classification process. We used MIREX Dataset for this research. Psycholinguistic and stylistic features were used as lyrics features. Psycholinguistic feature was a feature that related to the category of emotion. In this research, CBE used to support the extraction process of psycholinguistic feature. Stylistic features related with usage of unique words in the lyrics, e.g. ‘ooh’, ‘ah’, ‘yeah’, etc. Energy, temporal and spectrum features were extracted for audio features.The best test result for music emotion classification was the application of Random Forest methods for lyrics and audio features. The value of F-measure was 56.8%

EFEKTIVITAS PENGGUNAAN STOPLIST KATA UMUM DARI DOKUMEN HASIL KLASIFIKASI PRETOPOLOGY

Dokumen teks bahasa Indonesia sangat melimpah dan setiap waktu bertambah. Dalam proses pencarian, banyak dokumen yang dihasilkan menjadi kurang relevan jika tidak sesuai dengan keinginan pengguna. Stoplist merupakan kumpulan kata yang “tidak relevanâ€, namun sering muncul dalam dokumen. Kata umum juga sering digunakan pada bidang tertentu sehingga dimungkinkan untuk dokumen sebidang akan ada kata umum yang sering muncul. Pada sistem temu kembali informasi, asumsi yang ada adalah dengan menghapus stoplist, maka mengurangi jumlah kata yang akan diproses. Adanya kata umum bidang, memungkinkan adanya mengurangan jumlah kata yang akan diproses juga. Dalam penelitian ini dilakukan ekstraksi kata umum dari dokumen hasil klasifikasi dan melakukan perbandingan efektifitas antara aplikasi pencarian-1 menggunakan penghapusan stoplist dengan aplikasi pencarian-2 menggunakan penghapusan stoplist dan kata umum. Hal ini dilakukan untuk mengetahui model pencarian dengan tingkat relevansi dan waktu proses pencarian dokumen yang lebih tinggi. Hasil uji coba klasifikasi pretopology dengan 25 dokumen teknik, 25 ekonomi dan 25 pertanian diperoleh nilai rata-rata recall dan precision sebesar 90% dan 76%. Dan uji coba pencarian dengan 6 query terhadap 746 dokumen pada aplikasi pencarian-1 diperoleh nilai rata-rata f-measure dan waktu proses adalah 30.6% dan 0.239 detik. Sedangkan aplikasi pencarian-2 dengan threshold kata umum 1% adalah 76.5% dan 0.098 detik. Sehingga dapat dikatakan bahwa aplikasi pencarian-2 (dengan menggunakan penghapusan stoplist dan kata umum) lebih efektif dari pada aplikasi pencarian-1.Kata kunci: Sistem temu kembali informasi, Stoplist, Klasifikasi Pretopology, Kata Umum

Pembekalan Pemrograman Dasar Komputer bagi Guru TIK dan Siswa Terpilih di Tiga Mitra SMA Kabupaten Bangkalan

Programming is a basic skill in computing (Teknologi Informasi dan Komunikasi–TIK) feld. In our school partners located at Bangkalan, the teachers of computing course have non-computing background (physics, mathematics, biology). Tis condition means that their programming skill is not good enough. Tis is certainly a suboptimal circumstance since teacher’s mastery of a subject has a lot to do with the success of the teaching-learning process. Computer olympics (olimpiade komputer) is a programming competition for high school students. It has two kinds of tests. Tey were mathematical logic and programming problem solving. Because of the lacking in teacher’s programming skill, the preparation event focused only on mathematical logic. Tis approach had led the students to pass through frst/city selection, but it was not enough to pass the second/provincial selection. In this community service, we gave programming learning modules to the school partners and also train them about pascal programming. Te initial targets were TIK teachers who were also the preparation event coaches. Afer several considerations, we asked the schools to also send their best students as participants for the training. Te purpose were to not only prepare the current participant team, but also to support the regeneration of future teams. By the end of this activity, our partners’ teachers and students have had a better pascal programming skills. Tis result is shown in the increasing scores they get in their pre, mid, and fnal training evaluations

Deep Convolutional Neural Network AlexNet and Squeezenet for Maize Leaf Diseases Image Classification

Maize productivity growth is expected to increase by the year. However, there are obstacles to achieving it. One of the causes is diseases attack. Generally, maize plant diseases are easily detected through the leaves. This article discusses maize leaf disease classification using computer vision with a convolutional neural network (CNN). It aims to compare the deep convolutional neural network (CNN) AlexNet and Squeezenet. The network also used optimization, stochastic gradient descent with momentum (SGDM). The dataset for this experiment was taken from PlantVillage with 3852 images with 4 classes i.e healthy, blight, spot, and rust. The data is divided into 3 parts: training, validation, and testing. Training and validation are 80%, the rest for testing. The results of training with cross-validation produce the best accuracy of 100% for AlexNet and Squeezenet. Furthermore, the best weights and biases are stored in the model for testing data classification. The recognition results using AlexNet showed 97.69% accuracy. While the results of Squeezenet 44.49% accuracy. From this experiment environment, it can be concluded that AlexNet is better than Squeezenet for maize leaf diseases classification

SISTEM TEMU KEMBALI INFORMASI DALAM MESIN PENCARIAN MENGGUNAKAN MODEL RUANG VEKTOR DAN INVERTED INDEX

The annual addition of Thesis Publication Paper of Electrical Engineering Post Graduate School has caused the requirement of faster and more reliable searching tools. The application of information retrieval system of each documents, is expected to address such requirement. The searching system currently applied has still been using word matching system based on keywords of the paper�s title or author. This has caused each document found is only relevant to its title or author, but not to its content. Therefore, many other relevant documents can�t be found. The information retrieval system has several parts to obtain results. Stemming is one of the preprocessing parts. This study performed analysis on search engine of information retrieval system for publication paper abstracts using preprocessing, inverted index, quantifying of tf-idf and vector space model. On system testing it�s found that the application of stemming can generate searched documents with recall rate greater then system without using stemming, amount to 84,7%. This means that stemming can improve search performance, especially for the completeness of the acquisition of the documents that the user wants

MadureseSet: Madurese-Indonesian Dataset

MadureseSet is a digitized version of the physical document of Kamus Lengkap Bahasa Madura-Indonesia (The Complete Dictionary of Madurese-Indonesian). It stores the list of lemmata in Madurese, i.e., 17809 basic lemmata and 53722 substitution lemmata, and their translation in Indonesian. The details of each lemma may include its pronunciation, part of speech, synonym and homonym relations, speech level, dialect, and loanword. The framework of dataset creation consists of three stages. First, the data extraction stage processes the scanned results of the physical document to produce corrected data in a text file. Second, the data structural review stage processes the text file in terms of the paragraph, homonym, synonym, linguistic, poem, short poem, proverb, and metaphor structures to create the data structure that best represents the information in the dictionary. Finally, the database construction stage builds the physical data model and populates the MadureseSet database. MadureseSet is validated by a Madurese language expert who is also the author of the physical document source of this dataset. Thus, this dataset can be a primary source for Natural Language Processing (NLP) research, especially for the Madurese language

COMPARISON OF LSTM AND GRU IN PREDICTING THE NUMBER OF DIABETIC PATIENTS

Diabetes is one of the chronic diseases that many people have. This diabetes disease experienced a significant\ud increase during the pandemic, which could cause numerous deaths. One way to help hospitals prevent too many diabetic patients is to predict the number of diabetic patients. We used the LSTM (Long Short-Term Memory) method to predict diabetic patients. The study was conducted using patient data from the Modopuro Health Center, Mojokerto Regency. The prediction process manually calculates the data, then looks for the correlation of the data according to the LSTM method, namely identifying the autocorrelation coefficients at two to three different time lags. The data observed is daily from January 2, 2021, to April 20, 2022, with as many as 345 data. From the calculation results, the RMSE value is 3.184, while the GRU produces an RMSE of 1.727. It concluded that the GRU could better predict the number of visits of diabetic patients in internal medicine polyclinics