Internet service providers (ISPs) conduct their business by providing Internet access features to their customers. The COVID-19 pandemic has shifted most activity being performed remotely using an Internet connection. As a result, the demand for Internet services increased by 50%. This significant rise in the appeal of Internet services needs to be overtaken by a notable increase in the service quality provided by ISPs. Service quality plays a great role for enterprises, including ISPs, in retaining consumer loyalty. Thus, modelling ISPs’ service quality is of great importance. Since a common technique to reveal service quality is a timely and costly pencil survey-based method, this work proposes a framework based on the Sentiment Analysis (SA) of the Twitter dataset to model service quality. The SA involves the majority voting of three machine learning algorithms namely Naïve Bayes, Multinomial Naïve Bayes and Bernoulli Naïve Bayes. Making use of Thaicon’s service quality metrics, this work proposes a formula to generate a rating of service quality accordingly. For the case studies, we examined two ISPs in Indonesia, i.e., By.U and MPWR. The framework successfully extracted the service quality rate of both ISPs, revealing that By.U is better in terms of service quality, as indicated by a service quality rate of 0.71. Meanwhile, MPWR outperforms By.U in terms of customer service.

Local fruits and vegetables contribute to food security in the area. Efforts to gather information about local fruits and vegetables is very important. This is due to the diversity of local fruit and vegetables are endangered due to the penetration of imported fruits and vegetables that increasingly dominate, changing consumption patterns and land use. This study examines the diversity of local fruits and vegetables in Jember. The research method using exploratory survey which aims to gather information with a qualitative approach. The research instrument is observation sheet, interview and documentation. The survey area covers 31 districts in Jember, namely:Subdistrict Kencong, Gumukmas, Puger, Wuluhan, Ambulu, Tempurejo, Silo, Mayang, Mumbulsari, Jenggawah, Ajung, Rambipuji, Balung, Umbulsari, Semboro, Jombang, Sumberbaru, Embankment, Bangsalsari, Panti, Sukorambi, Arjasa, Pakusari, Kalisat, Ledokombo, Sumberjambe, Sukowono, Jelbuk, Kaliwates, Sumbersari and Patrang, Respondents were interviewed are traders of fruit and vegetables in the market and the centers of the fruit. The results of this exploratory study found that 46 kinds of local fruits and 52 kinds of local vegetables. 52.2% met the local fruit is cultivated, ie 24 types of local fruits such as:Avocado, Grapes, Belimbing, Duku, Durian, Guava, Guava Water, Orange Semboro, Orange Large, longan, mango, mangosteen, Melinjo, jackfruit, pineapple, papaya, banana, rambutan, Salak, Sawo, passionfruit, soursop and breadfruit. 30.8% of local vegetables that have been cultivated at 16 types of local vegetables such as: Bawang Merah, Bawang leaves Cabbage, Cauliflower, chinese cabbage / mustard, Long Bean, Large Chilli, chilli, Mushroom, Tomato, eggplant, beans, cucumber, pumpkin Siam, Kale and Spinach,

Teknologi informasi kini telah merambah hampir setiap sendi dalam kehidupan masyarakat tanpa terkecuali pada bidang pendidikan. Bidang pendidikan menjadi hal yang menarik untuk dikembangkan dalam kurun lima tahun terakhir. Salah satunya yaitu dengan metode belajar dalam jaringan (daring). Penelitian ini bertujuan untuk mengetahui respon dari penerapan sistem pembelajaran berbasis E-learning pada mahasiswa berkebutuhan khusus (Difabel). Metode penelitian yang dipakai ialah Research and Development (RnD). Sedangkan metode analisa datanya menggunakan menggunakan ELR. Hasil dari penelitian ini diperoleh penilaian dari validator terkait desain konten E-learning sebesar 4,52. Sedangkan penilaian observer dari segi desain E-learning sebesar 4, materi pembelajaran sebesar 3,7 dan penilaian dari mahasiswa berkebutuhan khusus sebesar 4,3. Dapat diketahui bahwa faktor inovasi media pembelajaran yang perlu ditingkatkan.

The Covid-19 pandemic is closely related to immunity, a biological protection system found in the human body. This system can ward off free radicals that will enter, so it is not easy to catch the disease. If this system works properly, then a person can avoid virus attacks. Consuming fruits and vegetables is the best immune boosting way that can be done. The importance of knowing local fruits and vegetables so that it can increase immunity. Make sure fruits and vegetables are always on the diet, so that the body is always in the best condition. This research examines the diversity of local fruits and vegetables in Jember District which can increase immunity. The research method uses exploratory survey, which aims to collect information with a qualitative approach. The research instrument consists of: observation sheets, interview guidelines and documentation. The survey area covers 31 districts in Jember District. The results of the study found 28 types of local fruit that can increase immunity, namely : avocado, sweet starfruit, wuluh starfruit, cantaloupe, red dragon fruit, guava, crystal guava, lime, semboro orange, kedondong, manalagi mango, gadung mango, pineapple, papaya, banana ambon, banana berlin, banana kepok, banana nangka, banana raja, soursop, big orange, ciplukan, passion fruit, pomegranate, mangosteen and apple melon. Also found 18 types of local vegetables that can increase immunity, namely: pumpkin, tomatoes, green spinach, red spinach, leeks, oyong, large chilies, rawit chilies, katu leaves, moringa leaves, papaya leaves, kenikir, cabbage, ferns, celery, and cauliflower. Of the 28 types of local fruit found, as much as 44.8% has been cultivated. From 18 types of local vegetables, which has been cultivated by 38.9%.

Indihome is a product of PT Telekomunikasi Indonesia as an internet service provider or internet service provider (ISP) in Indonesia. Every product or service offered to the public certainly has its advantages and disadvantages, as well as Indihome. From the advantages and disadvantages of Indihome services, we can do a technique, namely sentiment analysis. In this study, sentiment analysis was carried out regarding public responses or reviews about IndiHome services on Twitter social media. This study uses a comparison of TF-IDF and Word2Vec feature extraction, and the classification method used is the nave Bayes classifier. The accuracy results obtained in this study were 96% using the TF-IDF feature extraction and testing was carried out using an unseen data test that was selected randomly resulting in an accuracy of 92%. While the accuracy value obtained by using the Word2Vec feature extraction is 60% by testing using unseen test data that was selected randomly resulting in an accuracy value of 44%.